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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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 826–850 of 1722 papers

TitleStatusHype
Causal Counterfactuals for Improving the Robustness of Reinforcement LearningCode1
Propensity score models are better when post-calibratedCode0
Inference and Denoise: Causal Inference-based Neural Speech EnhancementCode1
CausalBench: A Large-scale Benchmark for Network Inference from Single-cell Perturbation DataCode1
Formalizing Statistical Causality via Modal Logic—0
Causal DAG extraction from a library of books or videos/movies—0
Spectral Representation Learning for Conditional Moment Models—0
A Survey on Causal Representation Learning and Future Work for Medical Image AnalysisCode0
Sample-Specific Root Causal Inference with Latent Variables—0
Dynamic Survival Transformers for Causal Inference with Electronic Health Records—0
Causal Information Bottleneck Boosts Adversarial Robustness of Deep Neural Network—0
LMPriors: Pre-Trained Language Models as Task-Specific Priors—0
Causally-guided Regularization of Graph Attention Improves Generalizability—0
Granger causal inference on DAGs identifies genomic loci regulating transcriptionCode1
Vision Paper: Causal Inference for Interpretable and Robust Machine Learning in Mobility Analysis—0
A Mixing Time Lower Bound for a Simplified Version of BART—0
Distributionally Robust Causal Inference with Observational Data—0
Fair Effect Attribution in Parallel Online Experiments—0
On the Identifiability and Estimation of Causal Location-Scale Noise ModelsCode0
Developing a general-purpose clinical language inference model from a large corpus of clinical notes—0
Sample Constrained Treatment Effect EstimationCode0
Deep Counterfactual Estimation with Categorical Background VariablesCode1
Disentangling Causal Effects from Sets of Interventions in the Presence of Unobserved ConfoundersCode0
Causal and Counterfactual Views of Missing Data Models—0
DeepMed: Semiparametric Causal Mediation Analysis with Debiased Deep LearningCode0
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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