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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 1276–1300 of 1722 papers

TitleStatusHype
Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic ModelsCode0
Adaptive Doubly Robust Estimator from Non-stationary Logging Policy under a Convergence of Average Probability—0
Causal Estimation with Functional Confounders—0
Do-calculus enables estimation of causal effects in partially observed biomolecular pathwaysCode0
A Critical Look at the Consistency of Causal Estimation With Deep Latent Variable Models—0
Causal Inference for Time series Analysis: Problems, Methods and Evaluation—0
Selecting Treatment Effects Models for Domain Adaptation Using Causal Knowledge—0
Estimating a Directed Tree for Extremes—0
Dynamic Structural Impact of the COVID-19 Outbreak on the Stock Market and the Exchange Rate: A Cross-country Analysis Among BRICS NationsCode0
Generating Synthetic Text Data to Evaluate Causal Inference Methods—0
Patterns, predictions, and actions: A story about machine learning—0
Grab the Reins of Crowds: Estimating the Effects of Crowd Movement Guidance Using Causal InferenceCode0
Causal versions of Maximum Entropy and Principle of Insufficient Reason—0
Integer Programming for Causal Structure Learning in the Presence of Latent VariablesCode0
Feedback in Imitation Learning: The Three Regimes of Covariate Shift—0
Policy Analysis using Synthetic Controls in Continuous-TimeCode0
Customer Price Sensitivities in Competitive Automobile Insurance Markets—0
Intact-VAE: Estimating Treatment Effects under Unobserved Confounding—0
A Commonsense Reasoning Framework for Explanatory Emotion Attribution, Generation and Re-classificationCode0
Entropic Causal Inference: Identifiability and Finite Sample Results—0
A design of human-like robust AI machines in object identification—0
The Value Added of Machine Learning to Causal Inference: Evidence from Revisited Studies—0
Counterfactual Thinking for Long-tailed Information Extraction—0
Invariant Representations for Reinforcement Learning without Reconstruction—0
Variational Deterministic Uncertainty Quantification—0
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All datasetsIHDPJobsIDHP

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