{"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/6","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":6,"pages_in_order":18,"rows_per_page":100,"rows":[501,600],"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/5","next":"/task/causal-inference/papers/7","papers":[{"url":"/paper/learning-to-search-efficiently-for-causally","slug":"learning-to-search-efficiently-for-causally","title":"Learning to search efficiently for causally near-optimal treatments","date":"2020-07-02","arxiv_id":"2007.00973","repositories_listed":1,"syntology":null},{"url":"/paper/controlling-for-unknown-confounders-in","slug":"controlling-for-unknown-confounders-in","title":"Estimation of Causal Effects in the Presence of Unobserved Confounding in the Alzheimer's Continuum","date":"2020-06-23","arxiv_id":"2006.13135","repositories_listed":1,"syntology":null},{"url":"/paper/reco-a-large-scale-chinese-reading","slug":"reco-a-large-scale-chinese-reading","title":"ReCO: A Large Scale Chinese Reading Comprehension Dataset on Opinion","date":"2020-06-22","arxiv_id":"2006.12146","repositories_listed":1,"syntology":null},{"url":"/paper/causal-inference-of-brain-connectivity-from","slug":"causal-inference-of-brain-connectivity-from","title":"Causal inference of brain connectivity from fMRI with $ψ$-Learning Incorporated Linear non-Gaussian Acyclic Model ($ψ$-LiNGAM)","date":"2020-06-16","arxiv_id":"2006.09536","repositories_listed":1,"syntology":null},{"url":"/paper/learning-individually-inferred-communication","slug":"learning-individually-inferred-communication","title":"Learning Individually Inferred Communication for Multi-Agent Cooperation","date":"2020-06-11","arxiv_id":"2006.06455","repositories_listed":1,"syntology":null},{"url":"/paper/counterfactual-vqa-a-cause-effect-look-at","slug":"counterfactual-vqa-a-cause-effect-look-at","title":"Counterfactual VQA: A Cause-Effect Look at Language Bias","date":"2020-06-08","arxiv_id":"2006.04315","repositories_listed":1,"syntology":null},{"url":"/paper/wasserstein-random-forests-and-applications","slug":"wasserstein-random-forests-and-applications","title":"Wasserstein Random Forests and Applications in Heterogeneous Treatment Effects","date":"2020-06-08","arxiv_id":"2006.04709","repositories_listed":1,"syntology":null},{"url":"/paper/identifying-causal-structure-in-dynamical","slug":"identifying-causal-structure-in-dynamical","title":"Identifying Causal Structure in Dynamical Systems","date":"2020-06-06","arxiv_id":"2006.03906","repositories_listed":1,"syntology":null},{"url":"/paper/automatic-detection-of-influential-actors-in","slug":"automatic-detection-of-influential-actors-in","title":"Automatic Detection of Influential Actors in Disinformation Networks","date":"2020-05-21","arxiv_id":"2005.10879","repositories_listed":1,"syntology":null},{"url":"/paper/a-theoretical-treatment-of-conditional","slug":"a-theoretical-treatment-of-conditional","title":"On the power of conditional independence testing under model-X","date":"2020-05-12","arxiv_id":"2005.05506","repositories_listed":1,"syntology":null},{"url":"/paper/does-terrorism-trigger-online-hate-speech-on","slug":"does-terrorism-trigger-online-hate-speech-on","title":"Does Terrorism Trigger Online Hate Speech? On the Association of Events and Time Series","date":"2020-04-30","arxiv_id":"2004.14733","repositories_listed":1,"syntology":null},{"url":"/paper/machine-learning-for-causal-inference-on-the","slug":"machine-learning-for-causal-inference-on-the","title":"Machine learning for causal inference: on the use of cross-fit estimators","date":"2020-04-21","arxiv_id":"2004.10337","repositories_listed":1,"syntology":null},{"url":"/paper/parkca-causal-inference-with-partially-known","slug":"parkca-causal-inference-with-partially-known","title":"ParKCa: Causal Inference with Partially Known Causes","date":"2020-03-17","arxiv_id":"2003.07952","repositories_listed":1,"syntology":null},{"url":"/paper/invariant-causal-prediction-for-block-mdps","slug":"invariant-causal-prediction-for-block-mdps","title":"Invariant Causal Prediction for Block MDPs","date":"2020-03-12","arxiv_id":"2003.06016","repositories_listed":1,"syntology":null},{"url":"/paper/missdeepcausal-causal-inference-from-1","slug":"missdeepcausal-causal-inference-from-1","title":"MissDeepCausal: Causal Inference from Incomplete Data Using Deep Latent Variable Models","date":"2020-02-25","arxiv_id":"2002.10837","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"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) · 1 unverified","sample_list":"/paper/missdeepcausal-causal-inference-from-1#ran","syntology_url":"https://syntology.ai/paper/2002.10837","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.10837"}},"official":{"repos":["imkemayer/MissDeepCausal"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/a-survey-on-causal-inference","slug":"a-survey-on-causal-inference","title":"A Survey on Causal Inference","date":"2020-02-05","arxiv_id":"2002.02770","repositories_listed":1,"syntology":null},{"url":"/paper/quantile-causal-discovery","slug":"quantile-causal-discovery","title":"Quantile Causal Discovery","date":"2020-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/localized-debiased-machine-learning-efficient","slug":"localized-debiased-machine-learning-efficient","title":"Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and Beyond","date":"2019-12-30","arxiv_id":"1912.12945","repositories_listed":1,"syntology":null},{"url":"/paper/a-normative-theory-for-causal-inference-and","slug":"a-normative-theory-for-causal-inference-and","title":"A Normative Theory for Causal Inference and Bayes Factor Computation in Neural Circuits","date":"2019-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/deep-representation-learning-for","slug":"deep-representation-learning-for","title":"Deep representation learning for individualized treatment effect estimation using electronic health records","date":"2019-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-causal-inference-method-for-reducing-gender","slug":"a-causal-inference-method-for-reducing-gender","title":"A Causal Inference Method for Reducing Gender Bias in Word Embedding Relations","date":"2019-11-25","arxiv_id":"1911.10787","repositories_listed":1,"syntology":null},{"url":"/paper/causality-for-machine-learning","slug":"causality-for-machine-learning","title":"Causality for Machine Learning","date":"2019-11-24","arxiv_id":"1911.10500","repositories_listed":1,"syntology":null},{"url":"/paper/causally-denoise-word-embeddings-using-half","slug":"causally-denoise-word-embeddings-using-half","title":"Causally Denoise Word Embeddings Using Half-Sibling Regression","date":"2019-11-24","arxiv_id":"1911.10524","repositories_listed":1,"syntology":null},{"url":"/paper/causal-inference-using-bayesian-non","slug":"causal-inference-using-bayesian-non","title":"Bayesian nonparametric discontinuity design","date":"2019-11-15","arxiv_id":"1911.06722","repositories_listed":1,"syntology":null},{"url":"/paper/targeted-estimation-of-heterogeneous","slug":"targeted-estimation-of-heterogeneous","title":"Targeted Estimation of Heterogeneous Treatment Effect in Observational Survival Analysis","date":"2019-10-20","arxiv_id":"1910.08877","repositories_listed":1,"syntology":null},{"url":"/paper/optimising-individual-treatment-effect-using","slug":"optimising-individual-treatment-effect-using","title":"Optimising Individual-Treatment-Effect Using Bandits","date":"2019-10-16","arxiv_id":"1910.07265","repositories_listed":1,"syntology":null},{"url":"/paper/spike-based-causal-inference-for-weight-1","slug":"spike-based-causal-inference-for-weight-1","title":"Spike-based causal inference for weight alignment","date":"2019-10-03","arxiv_id":"1910.01689","repositories_listed":1,"syntology":null},{"url":"/paper/debiased-bayesian-inference-for-average","slug":"debiased-bayesian-inference-for-average","title":"Debiased Bayesian inference for average treatment effects","date":"2019-09-26","arxiv_id":"1909.12078","repositories_listed":1,"syntology":null},{"url":"/paper/counterfactual-cross-validation-effective","slug":"counterfactual-cross-validation-effective","title":"Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models","date":"2019-09-11","arxiv_id":"1909.05299","repositories_listed":1,"syntology":null},{"url":"/paper/large-scale-gene-network-causal-inference","slug":"large-scale-gene-network-causal-inference","title":"Large-Scale Local Causal Inference of Gene Regulatory Relationships","date":"2019-09-03","arxiv_id":"1909.03818","repositories_listed":1,"syntology":null},{"url":"/paper/190807822","slug":"190807822","title":"A Multi-level Neural Network for Implicit Causality Detection in Web Texts","date":"2019-08-18","arxiv_id":"1908.07822","repositories_listed":1,"syntology":null},{"url":"/paper/optimal-estimation-of-generalized-average","slug":"optimal-estimation-of-generalized-average","title":"Optimal Estimation of Generalized Average Treatment Effects using Kernel Optimal Matching","date":"2019-08-13","arxiv_id":"1908.04748","repositories_listed":1,"syntology":null},{"url":"/paper/a-neural-network-oracle-for-quantum","slug":"a-neural-network-oracle-for-quantum","title":"A neural network oracle for quantum nonlocality problems in networks","date":"2019-07-24","arxiv_id":"1907.10552","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":0,"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/a-neural-network-oracle-for-quantum#ran","syntology_url":"https://syntology.ai/paper/1907.10552","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.10552"}},"official":{"repos":["tkrivachy/neural-network-for-nonlocality-in-networks"],"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/reinforcement-knowledge-graph-reasoning-for","slug":"reinforcement-knowledge-graph-reasoning-for","title":"Reinforcement Knowledge Graph Reasoning for Explainable Recommendation","date":"2019-06-12","arxiv_id":"1906.05237","repositories_listed":1,"syntology":null},{"url":"/paper/learning-individual-treatment-effects-from","slug":"learning-individual-treatment-effects-from","title":"Learning Individual Causal Effects from Networked Observational Data","date":"2019-06-08","arxiv_id":"1906.03485","repositories_listed":1,"syntology":null},{"url":"/paper/gradient-based-neural-dag-learning","slug":"gradient-based-neural-dag-learning","title":"Gradient-Based Neural DAG Learning","date":"2019-06-05","arxiv_id":"1906.02226","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/gradient-based-neural-dag-learning#ran","syntology_url":"https://syntology.ai/paper/1906.02226","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.02226"}},"official":{"repos":["kurowasan/GraN-DAG"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/heterogeneous-causal-effects-with-imperfect","slug":"heterogeneous-causal-effects-with-imperfect","title":"Heterogeneous causal effects with imperfect compliance: a Bayesian machine learning approach","date":"2019-05-29","arxiv_id":"1905.12707","repositories_listed":1,"syntology":null},{"url":"/paper/dirac-delta-regression-conditional-density","slug":"dirac-delta-regression-conditional-density","title":"Dirac Delta Regression: Conditional Density Estimation with Clinical Trials","date":"2019-05-24","arxiv_id":"1905.10330","repositories_listed":1,"syntology":null},{"url":"/paper/machine-learning-in-policy-evaluation-new","slug":"machine-learning-in-policy-evaluation-new","title":"Machine learning in policy evaluation: new tools for causal inference","date":"2019-03-01","arxiv_id":"1903.00402","repositories_listed":1,"syntology":null},{"url":"/paper/weighted-tensor-completion-for-time-series","slug":"weighted-tensor-completion-for-time-series","title":"Weighted Tensor Completion for Time-Series Causal Inference","date":"2019-02-12","arxiv_id":"1902.04646","repositories_listed":1,"syntology":null},{"url":"/paper/when-causal-intervention-meets-image-masking","slug":"when-causal-intervention-meets-image-masking","title":"When Causal Intervention Meets Adversarial Examples and Image Masking for Deep Neural Networks","date":"2019-02-09","arxiv_id":"1902.03380","repositories_listed":1,"syntology":null},{"url":"/paper/improving-consequential-decision-making-under","slug":"improving-consequential-decision-making-under","title":"Fair Decisions Despite Imperfect Predictions","date":"2019-02-08","arxiv_id":"1902.02979","repositories_listed":1,"syntology":null},{"url":"/paper/variable-importance-clouds-a-way-to-explore","slug":"variable-importance-clouds-a-way-to-explore","title":"Variable Importance Clouds: A Way to Explore Variable Importance for the Set of Good Models","date":"2019-01-10","arxiv_id":"1901.03209","repositories_listed":1,"syntology":null},{"url":"/paper/estimating-buildings-parameters-over-time","slug":"estimating-buildings-parameters-over-time","title":"Estimating Buildings' Parameters over Time Including Prior Knowledge","date":"2019-01-09","arxiv_id":"1901.07469","repositories_listed":1,"syntology":null},{"url":"/paper/whittemore-an-embedded-domain-specific","slug":"whittemore-an-embedded-domain-specific","title":"Whittemore: An embedded domain specific language for causal programming","date":"2018-12-21","arxiv_id":"1812.11918","repositories_listed":1,"syntology":null},{"url":"/paper/weighted-risk-minimization-deep-learning","slug":"weighted-risk-minimization-deep-learning","title":"What is the Effect of Importance Weighting in Deep Learning?","date":"2018-12-08","arxiv_id":"1812.03372","repositories_listed":1,"syntology":null},{"url":"/paper/identifying-the-effect-of-persuasion","slug":"identifying-the-effect-of-persuasion","title":"Identifying the Effect of Persuasion","date":"2018-12-06","arxiv_id":"1812.02276","repositories_listed":1,"syntology":null},{"url":"/paper/representation-learning-for-treatment-effect","slug":"representation-learning-for-treatment-effect","title":"Representation Learning for Treatment Effect Estimation from Observational Data","date":"2018-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-balancing-for-causal-inference","slug":"adversarial-balancing-for-causal-inference","title":"Adversarial Balancing for Causal Inference","date":"2018-10-17","arxiv_id":"1810.07406","repositories_listed":1,"syntology":{"n":4,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"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) · 3 unverified","sample_list":"/paper/adversarial-balancing-for-causal-inference#ran","syntology_url":"https://syntology.ai/paper/1810.07406","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.07406"}},"official":{"repos":["IBM/causallib"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/challenges-of-using-text-classifiers-for","slug":"challenges-of-using-text-classifiers-for","title":"Challenges of Using Text Classifiers for Causal Inference","date":"2018-10-01","arxiv_id":"1810.00956","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":0,"n_instrument":4,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"4 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; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/challenges-of-using-text-classifiers-for#ran","syntology_url":"https://syntology.ai/paper/1810.00956","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.00956"}},"official":{"repos":["zachwooddoughty/emnlp2018-causal"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/causal-inference-and-mechanism-clustering-of","slug":"causal-inference-and-mechanism-clustering-of","title":"Causal Inference and Mechanism Clustering of A Mixture of Additive Noise Models","date":"2018-09-23","arxiv_id":"1809.08568","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":0,"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/causal-inference-and-mechanism-clustering-of#ran","syntology_url":"https://syntology.ai/paper/1809.08568","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.08568"}},"official":{"repos":["amber0309/ANM-MM"],"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/surrogate-outcomes-and-transportability","slug":"surrogate-outcomes-and-transportability","title":"Surrogate Outcomes and Transportability","date":"2018-06-19","arxiv_id":"1806.07172","repositories_listed":1,"syntology":null},{"url":"/paper/orthogonal-random-forest-for-causal-inference","slug":"orthogonal-random-forest-for-causal-inference","title":"Orthogonal Random Forest for Causal Inference","date":"2018-06-09","arxiv_id":"1806.03467","repositories_listed":1,"syntology":null},{"url":"/paper/causal-inference-with-noisy-and-missing","slug":"causal-inference-with-noisy-and-missing","title":"Causal Inference with Noisy and Missing Covariates via Matrix Factorization","date":"2018-06-03","arxiv_id":"1806.00811","repositories_listed":1,"syntology":null},{"url":"/paper/counterfactual-mean-embedding-a-kernel-method","slug":"counterfactual-mean-embedding-a-kernel-method","title":"Counterfactual Mean Embeddings","date":"2018-05-22","arxiv_id":"1805.08845","repositories_listed":1,"syntology":null},{"url":"/paper/consistent-estimation-of-propensity-score","slug":"consistent-estimation-of-propensity-score","title":"Consistent Estimation of Propensity Score Functions with Oversampled Exposed Subjects","date":"2018-05-20","arxiv_id":"1805.07684","repositories_listed":1,"syntology":null},{"url":"/paper/a-constraint-based-algorithm-for-causal","slug":"a-constraint-based-algorithm-for-causal","title":"A Constraint-Based Algorithm For Causal Discovery with Cycles, Latent Variables and Selection Bias","date":"2018-05-05","arxiv_id":"1805.02087","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-generalized-method-of-moments","slug":"adversarial-generalized-method-of-moments","title":"Adversarial Generalized Method of Moments","date":"2018-03-19","arxiv_id":"1803.07164","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"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) · 1 unverified","sample_list":"/paper/adversarial-generalized-method-of-moments#ran","syntology_url":"https://syntology.ai/paper/1803.07164","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.07164"}},"official":{"repos":["vsyrgkanis/adversarial_gmm"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/nonparametric-quantile-based-causal-discovery","slug":"nonparametric-quantile-based-causal-discovery","title":"Distinguishing Cause from Effect Using Quantiles: Bivariate Quantile Causal Discovery","date":"2018-01-31","arxiv_id":"1801.10579","repositories_listed":1,"syntology":null},{"url":"/paper/matching-with-text-data-an-experimental","slug":"matching-with-text-data-an-experimental","title":"Matching with Text Data: An Experimental Evaluation of Methods for Matching Documents and of Measuring Match Quality","date":"2018-01-02","arxiv_id":"1801.00644","repositories_listed":1,"syntology":null},{"url":"/paper/rnn-based-counterfactual-time-series","slug":"rnn-based-counterfactual-time-series","title":"RNN-based counterfactual prediction, with an application to homestead policy and public schooling","date":"2017-12-10","arxiv_id":"1712.03553","repositories_listed":1,"syntology":null},{"url":"/paper/orthogonal-machine-learning-power-and","slug":"orthogonal-machine-learning-power-and","title":"Orthogonal Machine Learning: Power and Limitations","date":"2017-11-01","arxiv_id":"1711.00342","repositories_listed":1,"syntology":null},{"url":"/paper/domain-adaptation-by-using-causal-inference","slug":"domain-adaptation-by-using-causal-inference","title":"Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions","date":"2017-07-20","arxiv_id":"1707.06422","repositories_listed":1,"syntology":null},{"url":"/paper/on-adaptive-propensity-score-truncation-in","slug":"on-adaptive-propensity-score-truncation-in","title":"On Adaptive Propensity Score Truncation in Causal Inference","date":"2017-07-18","arxiv_id":"1707.05861","repositories_listed":1,"syntology":null},{"url":"/paper/deep-counterfactual-networks-with-propensity","slug":"deep-counterfactual-networks-with-propensity","title":"Deep Counterfactual Networks with Propensity-Dropout","date":"2017-06-19","arxiv_id":"1706.05966","repositories_listed":1,"syntology":null},{"url":"/paper/bias-and-high-dimensional-adjustment-in","slug":"bias-and-high-dimensional-adjustment-in","title":"Bias and high-dimensional adjustment in observational studies of peer effects","date":"2017-06-14","arxiv_id":"1706.04692","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-and-accurate-causal-inference-with","slug":"efficient-and-accurate-causal-inference-with","title":"Efficient and accurate causal inference with hidden confounders from genome-transcriptome variation data","date":"2017-04-17","arxiv_id":"1611.01114","repositories_listed":1,"syntology":null},{"url":"/paper/outcome-adaptive-lasso-variable-selection-for","slug":"outcome-adaptive-lasso-variable-selection-for","title":"Outcome-adaptive lasso: variable selection for causal inference","date":"2017-03-08","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/entropic-causal-inference","slug":"entropic-causal-inference","title":"Entropic Causal Inference","date":"2016-11-12","arxiv_id":"1611.04035","repositories_listed":1,"syntology":null},{"url":"/paper/ancestral-causal-inference","slug":"ancestral-causal-inference","title":"Ancestral Causal Inference","date":"2016-06-22","arxiv_id":"1606.07035","repositories_listed":1,"syntology":null},{"url":"/paper/learning-representations-for-counterfactual","slug":"learning-representations-for-counterfactual","title":"Learning Representations for Counterfactual Inference","date":"2016-05-12","arxiv_id":"1605.03661","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":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/learning-representations-for-counterfactual#ran","syntology_url":"https://syntology.ai/paper/1605.03661","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1605.03661"}},"official":null}},{"url":"/paper/recovery-of-non-linear-cause-effect","slug":"recovery-of-non-linear-cause-effect","title":"Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data","date":"2016-05-02","arxiv_id":"1605.00391","repositories_listed":1,"syntology":null},{"url":"/paper/merlin-mixture-effect-recovery-in-linear","slug":"merlin-mixture-effect-recovery-in-linear","title":"MERLiN: Mixture Effect Recovery in Linear Networks","date":"2015-12-03","arxiv_id":"1512.01255","repositories_listed":1,"syntology":null},{"url":"/paper/removing-systematic-errors-for-exoplanet","slug":"removing-systematic-errors-for-exoplanet","title":"Removing systematic errors for exoplanet search via latent causes","date":"2015-05-12","arxiv_id":"1505.03036","repositories_listed":1,"syntology":null},{"url":"/paper/towards-a-learning-theory-of-cause-effect","slug":"towards-a-learning-theory-of-cause-effect","title":"Towards a Learning Theory of Cause-Effect Inference","date":"2015-02-09","arxiv_id":"1502.02398","repositories_listed":1,"syntology":null},{"url":null,"slug":"estimating-interventional-distributions-with","title":"Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learning","date":"2025-07-07","arxiv_id":"2507.05526","repositories_listed":0,"syntology":null},{"url":null,"slug":"causal-aware-intelligent-qoe-optimization-for","title":"Causal-Aware Intelligent QoE Optimization for VR Interaction with Adaptive Keyframe Extraction","date":"2025-06-24","arxiv_id":"2506.19890","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantum-neural-networks-for-propensity-score","title":"Quantum Neural Networks for Propensity Score Estimation and Survival Analysis in Observational Biomedical Studies","date":"2025-06-24","arxiv_id":"2506.19973","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-evolutionary-swarm-architecture-a","title":"Bayesian Evolutionary Swarm Architecture: A Formal Epistemic System Grounded in Truth-Based Competition","date":"2025-06-23","arxiv_id":"2506.19191","repositories_listed":0,"syntology":null},{"url":null,"slug":"t-cpdl-a-temporal-causal-probabilistic","title":"T-CPDL: A Temporal Causal Probabilistic Description Logic for Developing Logic-RAG Agent","date":"2025-06-23","arxiv_id":"2506.18559","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-empirical-comparison-of-weak-iv-robust","title":"An Empirical Comparison of Weak-IV-Robust Procedures in Just-Identified Models","date":"2025-06-22","arxiv_id":"2506.18001","repositories_listed":0,"syntology":null},{"url":null,"slug":"causal-interventions-in-bond-multi-dealer-to","title":"Causal Interventions in Bond Multi-Dealer-to-Client Platforms","date":"2025-06-22","arxiv_id":"2506.18147","repositories_listed":0,"syntology":null},{"url":null,"slug":"international-trade-and-intellectual-property","title":"International Trade and Intellectual Property","date":"2025-06-21","arxiv_id":"2506.18929","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-prompts-to-constructs-a-dual-validity","title":"From Prompts to Constructs: A Dual-Validity Framework for LLM Research in Psychology","date":"2025-06-20","arxiv_id":"2506.16697","repositories_listed":0,"syntology":null},{"url":null,"slug":"linear-time-primitives-for-algorithm","title":"Linear-Time Primitives for Algorithm Development in Graphical Causal Inference","date":"2025-06-18","arxiv_id":"2506.15758","repositories_listed":0,"syntology":null},{"url":null,"slug":"double-machine-learning-for-conditional","title":"Double Machine Learning for Conditional Moment Restrictions: IV Regression, Proximal Causal Learning and Beyond","date":"2025-06-17","arxiv_id":"2506.14950","repositories_listed":0,"syntology":null},{"url":null,"slug":"estimation-of-treatment-effects-in-extreme","title":"Estimation of Treatment Effects in Extreme and Unobserved Data","date":"2025-06-16","arxiv_id":"2506.14051","repositories_listed":0,"syntology":null},{"url":null,"slug":"honesty-in-causal-forests-when-it-helps-and","title":"Honesty in Causal Forests: When It Helps and When It Hurts","date":"2025-06-16","arxiv_id":"2506.13107","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-mimic-datasets-for-better-digital","title":"Leveraging MIMIC Datasets for Better Digital Health: A Review on Open Problems, Progress Highlights, and Future Promises","date":"2025-06-15","arxiv_id":"2506.12808","repositories_listed":0,"syntology":null},{"url":null,"slug":"rethinking-distributional-ivs-kan-powered-d","title":"Rethinking Distributional IVs: KAN-Powered D-IV-LATE & Model Choice","date":"2025-06-15","arxiv_id":"2506.12765","repositories_listed":0,"syntology":null},{"url":null,"slug":"directed-acyclic-graph-convolutional-networks","title":"Directed Acyclic Graph Convolutional Networks","date":"2025-06-13","arxiv_id":"2506.12218","repositories_listed":0,"syntology":null},{"url":null,"slug":"foundation-models-for-causal-inference-via","title":"Foundation Models for Causal Inference via Prior-Data Fitted Networks","date":"2025-06-12","arxiv_id":"2506.10914","repositories_listed":0,"syntology":null},{"url":null,"slug":"2506-10179","title":"Correlation vs causation in Alzheimer's disease: an interpretability-driven study","date":"2025-06-11","arxiv_id":"2506.10179","repositories_listed":0,"syntology":null},{"url":null,"slug":"stoat-spatial-temporal-probabilistic-causal","title":"STOAT: Spatial-Temporal Probabilistic Causal Inference Network","date":"2025-06-11","arxiv_id":"2506.09544","repositories_listed":0,"syntology":null},{"url":null,"slug":"revolutionizing-clinical-trials-a-manifesto","title":"Revolutionizing Clinical Trials: A Manifesto for AI-Driven Transformation","date":"2025-06-10","arxiv_id":"2506.09102","repositories_listed":0,"syntology":null},{"url":null,"slug":"half-avae-adversarial-enhanced-factorized-and","title":"Half-AVAE: Adversarial-Enhanced Factorized and Structured Encoder-Free VAE for Underdetermined Independent Component Analysis","date":"2025-06-08","arxiv_id":"2506.07011","repositories_listed":0,"syntology":null},{"url":null,"slug":"investigating-the-relationship-between-6","title":"Investigating the Relationship Between Physical Activity and Tailored Behavior Change Messaging: Connecting Contextual Bandit with Large Language Models","date":"2025-06-08","arxiv_id":"2506.07275","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantile-optimal-policy-learning-under","title":"Quantile-Optimal Policy Learning under Unmeasured Confounding","date":"2025-06-08","arxiv_id":"2506.07140","repositories_listed":0,"syntology":null},{"url":null,"slug":"ad-ee-early-exiting-for-fast-and-reliable","title":"AD-EE: Early Exiting for Fast and Reliable Vision-Language Models in Autonomous Driving","date":"2025-06-04","arxiv_id":"2506.05404","repositories_listed":0,"syntology":null},{"url":null,"slug":"what-makes-treatment-effects-identifiable","title":"What Makes Treatment Effects Identifiable? Characterizations and Estimators Beyond Unconfoundedness","date":"2025-06-04","arxiv_id":"2506.04194","repositories_listed":0,"syntology":null}],"record_sha256":"38d4a0e7e00b9f0e47ee783f68bc3061143b94a28fac6ecca20e7e582e8dd705","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}