SOTAVerified

Causal Inference

Causal inference is the task of drawing a conclusion about a causal connection based on the conditions of the occurrence of an effect.

( Image credit: Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data )

Papers

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Showing 1501–1550 of 1722 papers

TitleStatusHype
CausalRec: Causal Inference for Visual Debiasing in Visually-Aware RecommendationCode0
Understand Waiting Time in Transaction Fee Mechanism: An Interdisciplinary PerspectiveCode0
How Fragile is Relation Extraction under Entity Replacements?Code0
Entropic Causal InferenceCode0
Can Large Language Models (or Humans) Disentangle Text?Code0
Outcome-adaptive lasso: variable selection for causal inferenceCode0
A Normative Theory for Causal Inference and Bayes Factor Computation in Neural CircuitsCode0
Towards a Learning Theory of Cause-Effect InferenceCode0
Learning Representations for Counterfactual InferenceCode0
Environment Invariant Linear Least SquaresCode0
Learning Representations of Instruments for Partial Identification of Treatment EffectsCode0
Equivariance Allows Handling Multiple Nuisance Variables When Analyzing Pooled Neuroimaging DatasetsCode0
A Survey on Causal Representation Learning and Future Work for Medical Image AnalysisCode0
Learning sources of variability from high-dimensional observational studiesCode0
Sample Constrained Treatment Effect EstimationCode0
Estimating Buildings' Parameters over Time Including Prior KnowledgeCode0
Learning the Causal Structure of Networked Dynamical Systems under Latent Nodes and Structured NoiseCode0
Estimating Causal Effects of Multi-Aspect Online Reviews with Multi-Modal ProxiesCode0
A Survey on Causal InferenceCode0
Causal prediction models for medication safety monitoring: The diagnosis of vancomycin-induced acute kidney injuryCode0
Learning to search efficiently for causally near-optimal treatmentsCode0
Learning Treatment Effects in Panels with General Intervention PatternsCode0
Estimating Causal Effects with the Neural Autoregressive Density EstimatorCode0
ParKCa: Causal Inference with Partially Known CausesCode0
Learning When to Treat Business Processes: Prescriptive Process Monitoring with Causal Inference and Reinforcement LearningCode0
Word Embeddings via Causal Inference: Gender Bias Reducing and Semantic Information PreservingCode0
Estimating heterogeneous survival treatment effect in observational data using machine learningCode0
AcceleratedLiNGAM: Learning Causal DAGs at the speed of GPUsCode0
Scalable Causal Domain AdaptationCode0
Treatment effects without multicollinearity? Temporal order and the Gram-Schmidt process in causal inferenceCode0
Estimating Individual Treatment Effects using Non-Parametric Regression Models: a ReviewCode0
A Fundamental Probabilistic Fuzzy Logic Framework Suitable for Causal ReasoningCode0
Causally motivated Shortcut Removal Using Auxiliary LabelsCode0
Leveraging text data for causal inference using electronic health recordsCode0
Lifted Causal Inference in Relational DomainsCode0
Variance Minimization in the Wasserstein Space for Invariant Causal PredictionCode0
Causally Denoise Word Embeddings Using Half-Sibling RegressionCode0
Paths to Causality: Finding Informative Subgraphs Within Knowledge Graphs for Knowledge-Based Causal DiscoveryCode0
Causality-oriented robustness: exploiting general noise interventionsCode0
Weather2vec: Representation Learning for Causal Inference with Non-Local Confounding in Air Pollution and Climate StudiesCode0
Testing Causal Models with Hidden Variables in Polynomial Delay via Conditional IndependenciesCode0
Performance of Cross-Validated Targeted Maximum Likelihood EstimationCode0
Calibration Strategies for Robust Causal Estimation: Theoretical and Empirical Insights on Propensity Score-Based EstimatorsCode0
A Causal Inference Method for Reducing Gender Bias in Word Embedding RelationsCode0
Estimation Beyond Data Reweighting: Kernel Method of MomentsCode0
LLMScan: Causal Scan for LLM Misbehavior DetectionCode0
An Experimental Design for Anytime-Valid Causal Inference on Multi-Armed BanditsCode0
What is the Effect of Importance Weighting in Deep Learning?Code0
Local Discovery by Partitioning: Polynomial-Time Causal Discovery Around Exposure-Outcome PairsCode0
A Simple Unified Approach to Testing High-Dimensional Conditional Independences for Categorical and Ordinal DataCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Random ForestAverage Treatment Effect Error0.96—Unverified
2Balancing Linear RegressionAverage Treatment Effect Error0.93—Unverified
3k-NNAverage Treatment Effect Error0.79—Unverified
4CEVAEAverage Treatment Effect Error0.46—Unverified
5Balancing Neural NetworkAverage Treatment Effect Error0.42—Unverified
6Causal ForestAverage Treatment Effect Error0.4—Unverified
7BCAUS DRAverage Treatment Effect Error0.29—Unverified
8TARNetAverage Treatment Effect Error0.28—Unverified
9Counterfactual Regression + WASSAverage Treatment Effect Error0.27—Unverified
10MTDL-KNNAverage Treatment Effect Error0.23—Unverified
#ModelMetricClaimedVerifiedStatus
1CFR WASSAverage Treatment Effect on the Treated Error0.09—Unverified
2CFR MMDAverage Treatment Effect on the Treated Error0.08—Unverified
3BARTAverage Treatment Effect on the Treated Error0.08—Unverified
4GANITEAverage Treatment Effect on the Treated Error0.06—Unverified
5BCAUSSAverage Treatment Effect on the Treated Error0.05—Unverified
#ModelMetricClaimedVerifiedStatus
1BARTAverage Treatment Effect Error0.34—Unverified
2OLS with separate regressors for each treatmentAverage Treatment Effect Error0.31—Unverified
3Average Treatment Effect Error-0.23—Unverified