{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/causal-inference/papers/3","list_of":"/task/causal-inference","task":"Causal Inference","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":3,"pages_in_order":18,"rows_per_page":100,"rows":[201,300],"of":1722,"counts":{"archive_papers_tagged":1722,"with_a_code_link":575,"where_syntology_ran_a_sample":139,"not_listed_spam_title":0,"listed":1722,"listed_where_code_ran":139,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":116,"every_run_a_failure_of_syntologys_instrument":23,"listed_with_a_run_with_no_instrument_failure":116,"listed_every_run_a_failure_of_syntologys_instrument":23,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/causal-inference","prev":"/task/causal-inference/papers/2","next":"/task/causal-inference/papers/4","papers":[{"url":"/paper/diner-debiasing-aspect-based-sentiment","slug":"diner-debiasing-aspect-based-sentiment","title":"DINER: Debiasing Aspect-based Sentiment Analysis with Multi-variable Causal Inference","date":"2024-03-02","arxiv_id":"2403.01166","repositories_listed":1,"syntology":null},{"url":"/paper/history-dependence-shapes-causal-inference-of","slug":"history-dependence-shapes-causal-inference-of","title":"History-dependence shapes causal inference of brain-behaviour relationships","date":"2024-03-01","arxiv_id":"2403.00947","repositories_listed":1,"syntology":null},{"url":"/paper/language-models-represent-beliefs-of-self-and","slug":"language-models-represent-beliefs-of-self-and","title":"Language Models Represent Beliefs of Self and Others","date":"2024-02-28","arxiv_id":"2402.18496","repositories_listed":1,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/language-models-represent-beliefs-of-self-and#ran","syntology_url":"https://syntology.ai/paper/2402.18496","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.18496"}},"official":null}},{"url":"/paper/unveiling-the-potential-of-robustness-in","slug":"unveiling-the-potential-of-robustness-in","title":"Unveiling the Potential of Robustness in Selecting Conditional Average Treatment Effect Estimators","date":"2024-02-28","arxiv_id":"2402.18392","repositories_listed":1,"syntology":{"n":10,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":10,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/unveiling-the-potential-of-robustness-in#ran","syntology_url":"https://syntology.ai/paper/2402.18392","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.18392"}},"official":{"repos":["yiyhuang3/cate_estimator_selection"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/causal-orthogonalization-multicollinearity","slug":"causal-orthogonalization-multicollinearity","title":"Treatment effects without multicollinearity? Temporal order and the Gram-Schmidt process in causal inference","date":"2024-02-27","arxiv_id":"2402.17103","repositories_listed":1,"syntology":null},{"url":"/paper/ros-causal-a-ros-based-causal-analysis","slug":"ros-causal-a-ros-based-causal-analysis","title":"ROS-Causal: A ROS-based Causal Analysis Framework for Human-Robot Interaction Applications","date":"2024-02-25","arxiv_id":"2402.16068","repositories_listed":1,"syntology":null},{"url":"/paper/rao-blackwellising-bayesian-causal-inference","slug":"rao-blackwellising-bayesian-causal-inference","title":"Effective Bayesian Causal Inference via Structural Marginalisation and Autoregressive Orders","date":"2024-02-22","arxiv_id":"2402.14781","repositories_listed":1,"syntology":null},{"url":"/paper/graph-out-of-distribution-generalization-via","slug":"graph-out-of-distribution-generalization-via","title":"Graph Out-of-Distribution Generalization via Causal Intervention","date":"2024-02-18","arxiv_id":"2402.11494","repositories_listed":1,"syntology":{"n":8,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":8,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/graph-out-of-distribution-generalization-via#ran","syntology_url":"https://syntology.ai/paper/2402.11494","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.11494"}},"official":{"repos":["fannie1208/canet"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/a-flexible-bayesian-g-formula-for-causal","slug":"a-flexible-bayesian-g-formula-for-causal","title":"A flexible Bayesian g-formula for causal survival analyses with time-dependent confounding","date":"2024-02-04","arxiv_id":"2402.02306","repositories_listed":1,"syntology":null},{"url":"/paper/bayesian-causal-inference-with-gaussian","slug":"bayesian-causal-inference-with-gaussian","title":"Bayesian Causal Inference with Gaussian Process Networks","date":"2024-02-01","arxiv_id":"2402.00623","repositories_listed":1,"syntology":null},{"url":"/paper/explaining-text-classifiers-with","slug":"explaining-text-classifiers-with","title":"Explaining Text Classifiers with Counterfactual Representations","date":"2024-02-01","arxiv_id":"2402.00711","repositories_listed":1,"syntology":null},{"url":"/paper/slang-new-concept-comprehension-of-large","slug":"slang-new-concept-comprehension-of-large","title":"SLANG: New Concept Comprehension of Large Language Models","date":"2024-01-23","arxiv_id":"2401.12585","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":1,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/slang-new-concept-comprehension-of-large#ran","syntology_url":"https://syntology.ai/paper/2401.12585","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.12585"}},"official":{"repos":["meirtz/focusonslang-toolbox"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-learning-based-group-causal-inference-in","slug":"deep-learning-based-group-causal-inference-in","title":"Deep Learning-based Group Causal Inference in Multivariate Time-series","date":"2024-01-16","arxiv_id":"2401.08386","repositories_listed":1,"syntology":null},{"url":"/paper/proximal-causal-inference-with-text-data","slug":"proximal-causal-inference-with-text-data","title":"Proximal Causal Inference With Text Data","date":"2024-01-12","arxiv_id":"2401.06687","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/proximal-causal-inference-with-text-data#ran","syntology_url":"https://syntology.ai/paper/2401.06687","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.06687"}},"official":{"repos":["jacobmchen/proximal_w_text"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/valid-causal-inference-with-unobserved","slug":"valid-causal-inference-with-unobserved","title":"Valid causal inference with unobserved confounding in high-dimensional settings","date":"2024-01-12","arxiv_id":"2401.06564","repositories_listed":1,"syntology":null},{"url":"/paper/neural-causal-abstractions","slug":"neural-causal-abstractions","title":"Neural Causal Abstractions","date":"2024-01-05","arxiv_id":"2401.02602","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/neural-causal-abstractions#ran","syntology_url":"https://syntology.ai/paper/2401.02602","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.02602"}},"official":{"repos":["causalailab/neuralcausalabstractions"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/modular-learning-of-deep-causal-generative","slug":"modular-learning-of-deep-causal-generative","title":"Modular Learning of Deep Causal Generative Models for High-dimensional Causal Inference","date":"2024-01-02","arxiv_id":"2401.01426","repositories_listed":1,"syntology":{"n":15,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":15,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/modular-learning-of-deep-causal-generative#ran","syntology_url":"https://syntology.ai/paper/2401.01426","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.01426"}},"official":{"repos":["musfiqshohan/modular-dcm"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/zero-shot-causal-graph-extrapolation-from","slug":"zero-shot-causal-graph-extrapolation-from","title":"Zero-shot Causal Graph Extrapolation from Text via LLMs","date":"2023-12-22","arxiv_id":"2312.14670","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/zero-shot-causal-graph-extrapolation-from#ran","syntology_url":"https://syntology.ai/paper/2312.14670","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.14670"}},"official":{"repos":["idsia-papers/causal-llms"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/invariant-anomaly-detection-under-1","slug":"invariant-anomaly-detection-under-1","title":"Invariant Anomaly Detection under Distribution Shifts: A Causal Perspective","date":"2023-12-21","arxiv_id":"2312.14329","repositories_listed":1,"syntology":null},{"url":"/paper/brain-inspired-visual-odometry-balancing","slug":"brain-inspired-visual-odometry-balancing","title":"Brain-Inspired Visual Odometry: Balancing Speed and Interpretability through a System of Systems Approach","date":"2023-12-20","arxiv_id":"2312.13162","repositories_listed":1,"syntology":null},{"url":"/paper/corecode-a-common-sense-annotated-dialogue","slug":"corecode-a-common-sense-annotated-dialogue","title":"CORECODE: A Common Sense Annotated Dialogue Dataset with Benchmark Tasks for Chinese Large Language Models","date":"2023-12-20","arxiv_id":"2312.12853","repositories_listed":1,"syntology":null},{"url":"/paper/interpretable-causal-inference-for-analyzing","slug":"interpretable-causal-inference-for-analyzing","title":"Interpretable Causal Inference for Analyzing Wearable, Sensor, and Distributional Data","date":"2023-12-17","arxiv_id":"2312.10569","repositories_listed":1,"syntology":null},{"url":"/paper/double-machine-learning-for-static-panel","slug":"double-machine-learning-for-static-panel","title":"Double Machine Learning for Static Panel Models with Fixed Effects","date":"2023-12-13","arxiv_id":"2312.08174","repositories_listed":1,"syntology":null},{"url":"/paper/learning-the-causal-structure-of-networked","slug":"learning-the-causal-structure-of-networked","title":"Learning the Causal Structure of Networked Dynamical Systems under Latent Nodes and Structured Noise","date":"2023-12-10","arxiv_id":"2312.05974","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/learning-the-causal-structure-of-networked#ran","syntology_url":"https://syntology.ai/paper/2312.05974","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.05974"}},"official":{"repos":["seabrapt/brain_underlying_structure_identification"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/marginal-density-ratio-for-off-policy-1","slug":"marginal-density-ratio-for-off-policy-1","title":"Marginal Density Ratio for Off-Policy Evaluation in Contextual Bandits","date":"2023-12-03","arxiv_id":"2312.01457","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/marginal-density-ratio-for-off-policy-1#ran","syntology_url":"https://syntology.ai/paper/2312.01457","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.01457"}},"official":{"repos":["faaizt/mr-ope"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/space-the-spatial-confounding-environment","slug":"space-the-spatial-confounding-environment","title":"SpaCE: The Spatial Confounding Environment","date":"2023-12-01","arxiv_id":"2312.00710","repositories_listed":1,"syntology":null},{"url":"/paper/fedeca-a-federated-external-control-arm","slug":"fedeca-a-federated-external-control-arm","title":"FedECA: A Federated External Control Arm Method for Causal Inference with Time-To-Event Data in Distributed Settings","date":"2023-11-28","arxiv_id":"2311.16984","repositories_listed":1,"syntology":null},{"url":"/paper/a-neural-framework-for-generalized-causal","slug":"a-neural-framework-for-generalized-causal","title":"A Neural Framework for Generalized Causal Sensitivity Analysis","date":"2023-11-27","arxiv_id":"2311.16026","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":2,"n_ran_checked":2,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":6,"phrase":"3 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/a-neural-framework-for-generalized-causal#ran","syntology_url":"https://syntology.ai/paper/2311.16026","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.16026"}},"official":{"repos":["dennisfrauen/neuralcsa"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/causal-structure-learning-supervised-by-large","slug":"causal-structure-learning-supervised-by-large","title":"Causal Structure Learning Supervised by Large Language Model","date":"2023-11-20","arxiv_id":"2311.11689","repositories_listed":1,"syntology":null},{"url":"/paper/an-adaptive-denoising-recommendation","slug":"an-adaptive-denoising-recommendation","title":"An adaptive denoising recommendation algorithm for causal separation bias","date":"2023-11-15","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/causal-prediction-models-for-medication","slug":"causal-prediction-models-for-medication","title":"Causal prediction models for medication safety monitoring: The diagnosis of vancomycin-induced acute kidney injury","date":"2023-11-15","arxiv_id":"2311.09137","repositories_listed":1,"syntology":null},{"url":"/paper/do-large-language-models-and-humans-have","slug":"do-large-language-models-and-humans-have","title":"Do large language models and humans have similar behaviors in causal inference with script knowledge?","date":"2023-11-13","arxiv_id":"2311.07311","repositories_listed":1,"syntology":null},{"url":"/paper/causal-inference-from-text-unveiling","slug":"causal-inference-from-text-unveiling","title":"Causal Inference from Text: Unveiling Interactions between Variables","date":"2023-11-09","arxiv_id":"2311.05286","repositories_listed":1,"syntology":null},{"url":"/paper/causalcite-a-causal-formulation-of-paper","slug":"causalcite-a-causal-formulation-of-paper","title":"CausalCite: A Causal Formulation of Paper Citations","date":"2023-11-05","arxiv_id":"2311.02790","repositories_listed":1,"syntology":null},{"url":"/paper/text-transport-toward-learning-causal-effects","slug":"text-transport-toward-learning-causal-effects","title":"Text-Transport: Toward Learning Causal Effects of Natural Language","date":"2023-10-31","arxiv_id":"2310.20697","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":8,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/text-transport-toward-learning-causal-effects#ran","syntology_url":"https://syntology.ai/paper/2310.20697","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.20697"}},"official":{"repos":["torylin/text-transport"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/causal-modeling-with-stationary-diffusions","slug":"causal-modeling-with-stationary-diffusions","title":"Causal Modeling with Stationary Diffusions","date":"2023-10-26","arxiv_id":"2310.17405","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/causal-modeling-with-stationary-diffusions#ran","syntology_url":"https://syntology.ai/paper/2310.17405","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.17405"}},"official":{"repos":["larslorch/stadion"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/fmmrec-fairness-aware-multimodal","slug":"fmmrec-fairness-aware-multimodal","title":"Causality-Inspired Fair Representation Learning for Multimodal Recommendation","date":"2023-10-26","arxiv_id":"2310.17373","repositories_listed":1,"syntology":null},{"url":"/paper/local-discovery-by-partitioning-polynomial","slug":"local-discovery-by-partitioning-polynomial","title":"Local Discovery by Partitioning: Polynomial-Time Causal Discovery Around Exposure-Outcome Pairs","date":"2023-10-25","arxiv_id":"2310.17816","repositories_listed":1,"syntology":null},{"url":"/paper/counterfactual-prediction-under-selective","slug":"counterfactual-prediction-under-selective","title":"Counterfactual Prediction Under Selective Confounding","date":"2023-10-21","arxiv_id":"2310.14064","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/counterfactual-prediction-under-selective#ran","syntology_url":"https://syntology.ai/paper/2310.14064","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.14064"}},"official":{"repos":["sohaib730/causalml"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/machine-learning-in-physics-a-short-guide","slug":"machine-learning-in-physics-a-short-guide","title":"Machine learning in physics: a short guide","date":"2023-10-16","arxiv_id":"2310.10368","repositories_listed":1,"syntology":null},{"url":"/paper/model-agnostic-covariate-assisted-inference","slug":"model-agnostic-covariate-assisted-inference","title":"Model-Agnostic Covariate-Assisted Inference on Partially Identified Causal Effects","date":"2023-10-12","arxiv_id":"2310.08115","repositories_listed":1,"syntology":{"n":11,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":11,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/model-agnostic-covariate-assisted-inference#ran","syntology_url":"https://syntology.ai/paper/2310.08115","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.08115"}},"official":{"repos":["amspector100/dual_bounds_paper"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/causal-unsupervised-semantic-segmentation","slug":"causal-unsupervised-semantic-segmentation","title":"Causal Unsupervised Semantic Segmentation","date":"2023-10-11","arxiv_id":"2310.07379","repositories_listed":1,"syntology":null},{"url":"/paper/high-dimensional-causal-inference-with","slug":"high-dimensional-causal-inference-with","title":"High Dimensional Causal Inference with Variational Backdoor Adjustment","date":"2023-10-09","arxiv_id":"2310.06100","repositories_listed":1,"syntology":null},{"url":"/paper/towards-causal-foundation-model-on-duality","slug":"towards-causal-foundation-model-on-duality","title":"Towards Causal Foundation Model: on Duality between Causal Inference and Attention","date":"2023-10-01","arxiv_id":"2310.00809","repositories_listed":1,"syntology":null},{"url":"/paper/algebraic-and-statistical-properties-of-the","slug":"algebraic-and-statistical-properties-of-the","title":"Algebraic and Statistical Properties of the Ordinary Least Squares Interpolator","date":"2023-09-27","arxiv_id":"2309.15769","repositories_listed":1,"syntology":null},{"url":"/paper/maximum-likelihood-estimation-of-latent","slug":"maximum-likelihood-estimation-of-latent","title":"Neural Network Parameter-optimization of Gaussian pmDAGs","date":"2023-09-25","arxiv_id":"2309.14073","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-zero-shot-chain-of-thought","slug":"enhancing-zero-shot-chain-of-thought","title":"Enhancing Zero-Shot Chain-of-Thought Reasoning in Large Language Models through Logic","date":"2023-09-23","arxiv_id":"2309.13339","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/enhancing-zero-shot-chain-of-thought#ran","syntology_url":"https://syntology.ai/paper/2309.13339","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.13339"}},"official":{"repos":["xf-zhao/lot"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/mpeg-a-multi-perspective-enhanced-graph","slug":"mpeg-a-multi-perspective-enhanced-graph","title":"MPEG: A Multi-Perspective Enhanced Graph Attention Network for Causal Emotion Entailment in Conversations","date":"2023-09-15","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/learning-continuous-valued-treatment-effects","slug":"learning-continuous-valued-treatment-effects","title":"Using representation balancing to learn conditional-average dose responses from clustered data","date":"2023-09-07","arxiv_id":"2309.03731","repositories_listed":1,"syntology":null},{"url":"/paper/granger-causal-inference-in-multivariate","slug":"granger-causal-inference-in-multivariate","title":"Granger Causal Inference in Multivariate Hawkes Processes by Minimum Message Length","date":"2023-09-05","arxiv_id":"2309.02027","repositories_listed":1,"syntology":null},{"url":"/paper/s-id-causal-effect-identification-in-a-sub","slug":"s-id-causal-effect-identification-in-a-sub","title":"s-ID: Causal Effect Identification in a Sub-Population","date":"2023-09-05","arxiv_id":"2309.02281","repositories_listed":1,"syntology":null},{"url":"/paper/causal-parrots-large-language-models-may-talk","slug":"causal-parrots-large-language-models-may-talk","title":"Causal Parrots: Large Language Models May Talk Causality But Are Not Causal","date":"2023-08-24","arxiv_id":"2308.13067","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":10,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/causal-parrots-large-language-models-may-talk#ran","syntology_url":"https://syntology.ai/paper/2308.13067","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.13067"}},"official":{"repos":["moritzwillig/causalparrots"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/slem-machine-learning-for-path-modeling-and","slug":"slem-machine-learning-for-path-modeling-and","title":"SLEM: Machine Learning for Path Modeling and Causal Inference with Super Learner Equation Modeling","date":"2023-08-08","arxiv_id":"2308.04365","repositories_listed":1,"syntology":null},{"url":"/paper/causal-effect-estimation-on-hierarchical","slug":"causal-effect-estimation-on-hierarchical","title":"Causal Effect Estimation on Hierarchical Spatial Graph Data","date":"2023-08-06","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/learning-sources-of-variability-from-high","slug":"learning-sources-of-variability-from-high","title":"Learning sources of variability from high-dimensional observational studies","date":"2023-07-26","arxiv_id":"2307.13868","repositories_listed":1,"syntology":null},{"url":"/paper/causality-oriented-robustness-exploiting","slug":"causality-oriented-robustness-exploiting","title":"Causality-oriented robustness: exploiting general noise interventions","date":"2023-07-18","arxiv_id":"2307.10299","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/causality-oriented-robustness-exploiting#ran","syntology_url":"https://syntology.ai/paper/2307.10299","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.10299"}},"official":{"repos":["xwshen51/drig"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/towards-trustworthy-explanation-on-causal","slug":"towards-trustworthy-explanation-on-causal","title":"Towards Trustworthy Explanation: On Causal Rationalization","date":"2023-06-25","arxiv_id":"2306.14115","repositories_listed":1,"syntology":null},{"url":"/paper/learning-conditional-instrumental-variable","slug":"learning-conditional-instrumental-variable","title":"Learning Conditional Instrumental Variable Representation for Causal Effect Estimation","date":"2023-06-21","arxiv_id":"2306.12453","repositories_listed":1,"syntology":null},{"url":"/paper/accelerating-generalized-random-forests-with","slug":"accelerating-generalized-random-forests-with","title":"Generalized Random Forests using Fixed-Point Trees","date":"2023-06-20","arxiv_id":"2306.11908","repositories_listed":1,"syntology":null},{"url":"/paper/identifiable-causal-inference-with-noisy","slug":"identifiable-causal-inference-with-noisy","title":"Identifiable causal inference with noisy treatment and no side information","date":"2023-06-18","arxiv_id":"2306.10614","repositories_listed":1,"syntology":null},{"url":"/paper/a-brief-review-of-hypernetworks-in-deep","slug":"a-brief-review-of-hypernetworks-in-deep","title":"A Brief Review of Hypernetworks in Deep Learning","date":"2023-06-12","arxiv_id":"2306.06955","repositories_listed":1,"syntology":{"n":12,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/a-brief-review-of-hypernetworks-in-deep#ran","syntology_url":"https://syntology.ai/paper/2306.06955","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.06955"}},"official":{"repos":["jmdvinodjmd/HyperITE"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/can-large-language-models-infer-causation","slug":"can-large-language-models-infer-causation","title":"Can Large Language Models Infer Causation from Correlation?","date":"2023-06-09","arxiv_id":"2306.05836","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/can-large-language-models-infer-causation#ran","syntology_url":"https://syntology.ai/paper/2306.05836","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.05836"}},"official":{"repos":["causalnlp/corr2cause"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/leveraging-text-data-for-causal-inference","slug":"leveraging-text-data-for-causal-inference","title":"Leveraging text data for causal inference using electronic health records","date":"2023-06-09","arxiv_id":"2307.03687","repositories_listed":1,"syntology":null},{"url":"/paper/finding-counterfactually-optimal-action","slug":"finding-counterfactually-optimal-action","title":"Finding Counterfactually Optimal Action Sequences in Continuous State Spaces","date":"2023-06-06","arxiv_id":"2306.03929","repositories_listed":1,"syntology":{"n":7,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":7,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/finding-counterfactually-optimal-action#ran","syntology_url":"https://syntology.ai/paper/2306.03929","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.03929"}},"official":{"repos":["networks-learning/counterfactual-continuous-mdp"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/inferring-causal-effects-under-heterogeneous","slug":"inferring-causal-effects-under-heterogeneous","title":"Inferring Individual Direct Causal Effects Under Heterogeneous Peer Influence","date":"2023-05-27","arxiv_id":"2305.17479","repositories_listed":1,"syntology":null},{"url":"/paper/dynamic-inter-treatment-information-sharing","slug":"dynamic-inter-treatment-information-sharing","title":"Dynamic Inter-treatment Information Sharing for Individualized Treatment Effects Estimation","date":"2023-05-25","arxiv_id":"2305.15984","repositories_listed":1,"syntology":null},{"url":"/paper/neuroevolutionary-representations-for","slug":"neuroevolutionary-representations-for","title":"Neuroevolutionary representations for learning heterogeneous treatment effects","date":"2023-05-25","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/covariate-balancing-using-the-integral","slug":"covariate-balancing-using-the-integral","title":"Covariate balancing using the integral probability metric for causal inference","date":"2023-05-23","arxiv_id":"2305.13715","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/covariate-balancing-using-the-integral#ran","syntology_url":"https://syntology.ai/paper/2305.13715","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.13715"}},"official":{"repos":["ggong369/cbipm"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/entred-benchmarking-relation-extraction-with","slug":"entred-benchmarking-relation-extraction-with","title":"How Fragile is Relation Extraction under Entity Replacements?","date":"2023-05-22","arxiv_id":"2305.13551","repositories_listed":1,"syntology":null},{"url":"/paper/uctrl-unbiased-contrastive-representation","slug":"uctrl-unbiased-contrastive-representation","title":"uCTRL: Unbiased Contrastive Representation Learning via Alignment and Uniformity for Collaborative Filtering","date":"2023-05-22","arxiv_id":"2305.12768","repositories_listed":1,"syntology":null},{"url":"/paper/estimation-beyond-data-reweighting-kernel","slug":"estimation-beyond-data-reweighting-kernel","title":"Estimation Beyond Data Reweighting: Kernel Method of Moments","date":"2023-05-18","arxiv_id":"2305.10898","repositories_listed":1,"syntology":null},{"url":"/paper/counterfactually-comparing-abstaining-1","slug":"counterfactually-comparing-abstaining-1","title":"Counterfactually Comparing Abstaining Classifiers","date":"2023-05-17","arxiv_id":"2305.10564","repositories_listed":1,"syntology":{"n":9,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/counterfactually-comparing-abstaining-1#ran","syntology_url":"https://syntology.ai/paper/2305.10564","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.10564"}},"official":{"repos":["yjchoe/comparingabstainingclassifiers"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/cola-contextualized-commonsense-causal","slug":"cola-contextualized-commonsense-causal","title":"COLA: Contextualized Commonsense Causal Reasoning from the Causal Inference Perspective","date":"2023-05-09","arxiv_id":"2305.05191","repositories_listed":1,"syntology":{"n":18,"n_ran":17,"n_constructed":0,"n_ran_checked":16,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":16,"n_pointer_only":2,"phrase":"17 ran (of which 0 constructed an object rather than computing a result; 16 with no instrument failure: 0 honoured, 0 violated, 16 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/cola-contextualized-commonsense-causal#ran","syntology_url":"https://syntology.ai/paper/2305.05191","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.05191"}},"official":{"repos":["hkust-knowcomp/cola"],"state":"official (archive's flag): 17 ran","n_ran":17,"n_constructed":0,"n_ran_no_instrument_failure":16,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/understand-waiting-time-in-transaction-fee","slug":"understand-waiting-time-in-transaction-fee","title":"Understand Waiting Time in Transaction Fee Mechanism: An Interdisciplinary Perspective","date":"2023-05-04","arxiv_id":"2305.02552","repositories_listed":1,"syntology":null},{"url":"/paper/double-and-single-descent-in-causal-inference","slug":"double-and-single-descent-in-causal-inference","title":"Double and Single Descent in Causal Inference with an Application to High-Dimensional Synthetic Control","date":"2023-05-01","arxiv_id":"2305.00700","repositories_listed":1,"syntology":null},{"url":"/paper/causal-fault-localisation-in-dataflow-systems","slug":"causal-fault-localisation-in-dataflow-systems","title":"Causal fault localisation in dataflow systems","date":"2023-04-24","arxiv_id":"2304.11987","repositories_listed":1,"syntology":null},{"url":"/paper/compositional-probabilistic-and-causal","slug":"compositional-probabilistic-and-causal","title":"Compositional Probabilistic and Causal Inference using Tractable Circuit Models","date":"2023-04-17","arxiv_id":"2304.08278","repositories_listed":1,"syntology":null},{"url":"/paper/pgmpy-a-python-toolkit-for-bayesian-networks","slug":"pgmpy-a-python-toolkit-for-bayesian-networks","title":"pgmpy: A Python Toolkit for Bayesian Networks","date":"2023-04-17","arxiv_id":"2304.08639","repositories_listed":1,"syntology":null},{"url":"/paper/bridging-nations-quantifying-the-role-of","slug":"bridging-nations-quantifying-the-role-of","title":"Bridging Nations: Quantifying the Role of Multilinguals in Communication on Social Media","date":"2023-04-07","arxiv_id":"2304.03797","repositories_listed":1,"syntology":null},{"url":"/paper/one-step-estimation-of-differentiable-hilbert","slug":"one-step-estimation-of-differentiable-hilbert","title":"One-Step Estimation of Differentiable Hilbert-Valued Parameters","date":"2023-03-29","arxiv_id":"2303.16711","repositories_listed":1,"syntology":null},{"url":"/paper/synthetic-combinations-a-causal-inference-1","slug":"synthetic-combinations-a-causal-inference-1","title":"Synthetic Combinations: A Causal Inference Framework for Combinatorial Interventions","date":"2023-03-24","arxiv_id":"2303.14226","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/synthetic-combinations-a-causal-inference-1#ran","syntology_url":"https://syntology.ai/paper/2303.14226","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.14226"}},"official":{"repos":["aagarwal1996/synth_combo"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/a-survey-on-causal-inference-for","slug":"a-survey-on-causal-inference-for","title":"A Survey on Causal Inference for Recommendation","date":"2023-03-21","arxiv_id":"2303.11666","repositories_listed":1,"syntology":null},{"url":"/paper/approaching-an-unknown-communication-system","slug":"approaching-an-unknown-communication-system","title":"Approaching an unknown communication system by latent space exploration and causal inference","date":"2023-03-20","arxiv_id":"2303.10931","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/approaching-an-unknown-communication-system#ran","syntology_url":"https://syntology.ai/paper/2303.10931","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.10931"}},"official":{"repos":["andleb/Approaching-an-unknown-communication-system"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-end-to-end-patient-representations","slug":"learning-end-to-end-patient-representations","title":"Learning end-to-end patient representations through self-supervised covariate balancing for causal treatment effect estimation","date":"2023-03-20","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/inference-on-optimal-dynamic-policies-via","slug":"inference-on-optimal-dynamic-policies-via","title":"Inference on Optimal Dynamic Policies via Softmax Approximation","date":"2023-03-08","arxiv_id":"2303.04416","repositories_listed":1,"syntology":null},{"url":"/paper/learning-when-to-treat-business-processes","slug":"learning-when-to-treat-business-processes","title":"Learning When to Treat Business Processes: Prescriptive Process Monitoring with Causal Inference and Reinforcement Learning","date":"2023-03-07","arxiv_id":"2303.03572","repositories_listed":1,"syntology":null},{"url":"/paper/environment-invariant-linear-least-squares","slug":"environment-invariant-linear-least-squares","title":"Environment Invariant Linear Least Squares","date":"2023-03-06","arxiv_id":"2303.03092","repositories_listed":1,"syntology":null},{"url":"/paper/page-a-position-aware-graph-based-model-for","slug":"page-a-position-aware-graph-based-model-for","title":"PAGE: A Position-Aware Graph-Based Model for Emotion Cause Entailment in Conversation","date":"2023-03-03","arxiv_id":"2303.01795","repositories_listed":1,"syntology":null},{"url":"/paper/hyperparameter-tuning-and-model-evaluation-in","slug":"hyperparameter-tuning-and-model-evaluation-in","title":"Hyperparameter Tuning and Model Evaluation in Causal Effect Estimation","date":"2023-03-02","arxiv_id":"2303.01412","repositories_listed":1,"syntology":null},{"url":"/paper/learning-high-dimensional-causal-effect","slug":"learning-high-dimensional-causal-effect","title":"Learning high-dimensional causal effect","date":"2023-03-01","arxiv_id":"2303.00821","repositories_listed":1,"syntology":null},{"url":"/paper/from-feature-importance-to-distance-metric-an","slug":"from-feature-importance-to-distance-metric-an","title":"Variable Importance Matching for Causal Inference","date":"2023-02-23","arxiv_id":"2302.11715","repositories_listed":1,"syntology":null},{"url":"/paper/debiasing-recommendation-by-learning","slug":"debiasing-recommendation-by-learning","title":"Debiasing Recommendation by Learning Identifiable Latent Confounders","date":"2023-02-10","arxiv_id":"2302.05052","repositories_listed":1,"syntology":null},{"url":"/paper/a-fast-bootstrap-algorithm-for-causal","slug":"a-fast-bootstrap-algorithm-for-causal","title":"A Fast Bootstrap Algorithm for Causal Inference with Large Data","date":"2023-02-06","arxiv_id":"2302.02859","repositories_listed":1,"syntology":null},{"url":"/paper/causal-shift-response-functions-with-neural","slug":"causal-shift-response-functions-with-neural","title":"Causal Estimation of Exposure Shifts with Neural Networks","date":"2023-02-06","arxiv_id":"2302.02560","repositories_listed":1,"syntology":null},{"url":"/paper/a-counterfactual-collaborative-session-based","slug":"a-counterfactual-collaborative-session-based","title":"A Counterfactual Collaborative Session-based Recommender System","date":"2023-01-31","arxiv_id":"2301.13364","repositories_listed":1,"syntology":null},{"url":"/paper/temporai-facilitating-machine-learning","slug":"temporai-facilitating-machine-learning","title":"TemporAI: Facilitating Machine Learning Innovation in Time Domain Tasks for Medicine","date":"2023-01-28","arxiv_id":"2301.12260","repositories_listed":1,"syntology":{"n":9,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/temporai-facilitating-machine-learning#ran","syntology_url":"https://syntology.ai/paper/2301.12260","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.12260"}},"official":{"repos":["vanderschaarlab/temporai"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/salesforce-causalai-library-a-fast-and","slug":"salesforce-causalai-library-a-fast-and","title":"Salesforce CausalAI Library: A Fast and Scalable Framework for Causal Analysis of Time Series and Tabular Data","date":"2023-01-25","arxiv_id":"2301.10859","repositories_listed":1,"syntology":null},{"url":"/paper/optimal-transport-for-counterfactual","slug":"optimal-transport-for-counterfactual","title":"Optimal Transport for Counterfactual Estimation: A Method for Causal Inference","date":"2023-01-18","arxiv_id":"2301.07755","repositories_listed":1,"syntology":null},{"url":"/paper/causal-falsification-of-digital-twins","slug":"causal-falsification-of-digital-twins","title":"Causal Falsification of Digital Twins","date":"2023-01-17","arxiv_id":"2301.07210","repositories_listed":1,"syntology":null},{"url":"/paper/causal-recurrent-variational-autoencoder-for","slug":"causal-recurrent-variational-autoencoder-for","title":"Causal Recurrent Variational Autoencoder for Medical Time Series Generation","date":"2023-01-16","arxiv_id":"2301.06574","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/causal-recurrent-variational-autoencoder-for#ran","syntology_url":"https://syntology.ai/paper/2301.06574","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.06574"}},"official":{"repos":["hongmingli1995/cr-vae"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}}],"record_sha256":"206e9e8688c47ad97ba06adeacb476cff76cde2759c7abdcc3dae6d88396ca2a","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}