Causal Inference

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

3 leaderboard tables shown for this task, 3 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

8 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

3 subtasks in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 575 papers with code (1,722 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Unbiased Scene Graph Generation from Biased Training
  27 Feb 2020 6 repositories listed Syntology ran 3 of 6 samples · 3 unverified · 6 pointer-only (licence)
  Today's scene graph generation (SGG) task is still far from practical, mainly due to the severe training bias, e.
• Causal Effect Inference with Deep Latent-Variable Models
  24 May 2017 6 repositories listed
  Learning individual-level causal effects from observational data, such as inferring the most effective medication for a specific patient, is a problem of growing importance for policy makers.
• DoWhy: An End-to-End Library for Causal Inference
  9 Nov 2020 5 repositories listed Syntology ran 6 of 6 samples · 0 unverified
  In addition to efficient statistical estimators of a treatment's effect, successful application of causal inference requires specifying assumptions about the mechanisms underlying observed data and testing whether they…
• Adapting Neural Networks for the Estimation of Treatment Effects
  5 Jun 2019 5 repositories listed Syntology ran 0 of 5 samples · 5 unverified · 3 pointer-only (licence)
  We propose two adaptations based on insights from the statistical literature on the estimation of treatment effects.
• Adapting Text Embeddings for Causal Inference
  29 May 2019 4 repositories listed
  To address this challenge, we develop causally sufficient embeddings, low-dimensional document representations that preserve sufficient information for causal identification and allow for efficient estimation of causal…
• Double/Debiased Machine Learning for Treatment and Causal Parameters
  30 Jul 2016 4 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  Fortunately, this regularization bias can be removed by solving auxiliary prediction problems via ML tools.
• Estimating individual treatment effect: generalization bounds and algorithms
  13 Jun 2016 4 repositories listed Syntology ran 0 of 5 samples · 5 unverified
  We give a novel, simple and intuitive generalization-error bound showing that the expected ITE estimation error of a representation is bounded by a sum of the standard generalization-error of that representation and the…
• Structural Intervention Distance (SID) for Evaluating Causal Graphs
  5 Jun 2013 4 repositories listed
  To quantify such differences, we propose a (pre-) distance between DAGs, the structural intervention distance (SID).
• Hierarchical NeuroSymbolic Approach for Comprehensive and Explainable Action Quality Assessment
  20 Mar 2024 3 repositories listed
  We found that domain experts prefer our system and find it more informative than purely neural approaches to AQA in diving.
• Federated Causal Inference from Observational Data
  24 Aug 2023 3 repositories listed Syntology ran 4 of 6 samples · 2 unverified · 6 pointer-only (licence)
  In this article, we propose a framework to estimate causal effects from decentralized data sources.
• Subset verification and search algorithms for causal DAGs
  9 Jan 2023 3 repositories listed Syntology ran 2 of 3 samples · 1 unverified
  In this work, we study the problem of identifying the smallest set of interventions required to learn the causal relationships between a subset of edges (target edges).
• Deep Reinforcement Learning for Multi-Agent Interaction
  2 Aug 2022 3 repositories listed
  The development of autonomous agents which can interact with other agents to accomplish a given task is a core area of research in artificial intelligence and machine learning.
• SUTD-TrafficQA: A Question Answering Benchmark and an Efficient Network for Video Reasoning over Traffic Events
  29 Mar 2021 3 repositories listed Syntology ran 1 of 9 samples · 8 unverified · 2 pointer-only (licence)
  In this paper, we create a novel dataset, SUTD-TrafficQA (Traffic Question Answering), which takes the form of video QA based on the collected 10, 080 in-the-wild videos and annotated 62, 535 QA pairs, for benchmarking…
• Disentangling User Interest and Conformity for Recommendation with Causal Embedding
  19 Jun 2020 3 repositories listed
  We further demonstrate that the learned embeddings successfully capture the desired causes, and show that DICE guarantees the robustness and interpretability of recommendation.
• Causal Inference under Outcome-Based Sampling with Monotonicity Assumptions
  17 Apr 2020 3 repositories listed
  We study causal inference under case-control and case-population sampling.
• Estimating Transfer Entropy via Copula Entropy
  10 Oct 2019 3 repositories listed
  Causal discovery is a fundamental problem in statistics and has wide applications in different fields.
• Using Embeddings to Correct for Unobserved Confounding in Networks
  11 Feb 2019 3 repositories listed
  We validate the method with experiments on a semi-synthetic social network dataset.
• Local Linear Forests
  30 Jul 2018 3 repositories listed
  Random forests are a powerful method for non-parametric regression, but are limited in their ability to fit smooth signals, and can show poor predictive performance in the presence of strong, smooth effects.
• Interpretable Almost Matching Exactly for Causal Inference
  18 Jun 2018 3 repositories listed
  Notable advantages of our method over existing matching procedures are its high-quality matches, versatility in handling different data distributions that may have irrelevant variables, and ability to handle missing…
• Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise
  4 Jun 2018 3 repositories listed Syntology ran 0 of 1 samples · 1 unverified · 1 pointer-only (licence)
  We introduce coroICA, confounding-robust independent component analysis, a novel ICA algorithm which decomposes linearly mixed multivariate observations into independent components that are corrupted (and rendered…
• An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls
  25 Dec 2017 3 repositories listed
  We introduce new inference procedures for counterfactual and synthetic control methods for policy evaluation.
• Counterfactual Fairness
  20 Mar 2017 3 repositories listed
  Machine learning can impact people with legal or ethical consequences when it is used to automate decisions in areas such as insurance, lending, hiring, and predictive policing.
• BART: Bayesian additive regression trees
  19 Jun 2008 3 repositories listed Syntology ran 1 of 1 samples · 0 unverified
  We develop a Bayesian "sum-of-trees" model where each tree is constrained by a regularization prior to be a weak learner, and fitting and inference are accomplished via an iterative Bayesian backfitting MCMC algorithm…
• Doubly Robust Inference on Causal Derivative Effects for Continuous Treatments
  12 Jan 2025 2 repositories listed
  In this paper, we investigate nonparametric inference on the derivative of the dose-response curve with and without the positivity condition.
• LLMScan: Causal Scan for LLM Misbehavior Detection
  22 Oct 2024 2 repositories listed
  Despite the success of Large Language Models (LLMs) across various fields, their potential to generate untruthful, biased and harmful responses poses significant risks, particularly in critical applications.
• Causal Evaluation of Language Models
  1 May 2024 2 repositories listed
  Recent advances in language models have expanded the horizons of artificial intelligence across various domains, sparking inquiries into their potential for causal reasoning.
• The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology
  17 Apr 2024 2 repositories listed Syntology ran 3 of 29 samples · 26 unverified
  The devices, which we call causal chambers, are computer-controlled laboratories that allow us to manipulate and measure an array of variables from these physical systems, providing a rich testbed for algorithms from a…
• CIDGMed: Causal Inference-Driven Medication Recommendation with Enhanced Dual-Granularity Learning
  1 Mar 2024 2 repositories listed
  Medication recommendation aims to integrate patients' long-term health records to provide accurate and safe medication combinations for specific health states.
• Causal hybrid modeling with double machine learning
  20 Feb 2024 2 repositories listed
  Hybrid modeling integrates machine learning with scientific knowledge to enhance interpretability, generalization, and adherence to natural laws.
• Conditional Generative Models are Sufficient to Sample from Any Causal Effect Estimand
  12 Feb 2024 2 repositories listed
  Our proposed algorithm follows the recursive steps of the existing likelihood-based identification algorithms to train a set of feed-forward models, and connect them in a specific way to sample from the desired…

Syntology lines on 11 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.