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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1576–1600 of 1722 papers

TitleStatusHype
Controlling for Unobserved Confounding with Large Language Model Classification of Patient Smoking Status—0
Controlling Learned Effects to Reduce Spurious Correlations in Text Classifiers—0
Cooperative Causal GraphSAGE—0
Correcting invalid regression discontinuity designs with multiple time period data—0
Counterfactual Analysis of the Impact of the IMF Program on Child Poverty in the Global-South Region using Causal-Graphical Normalizing Flows—0
Counterfactual-based Root Cause Analysis for Dynamical Systems—0
Counterfactual Estimation and Optimization of Click Metrics for Search Engines—0
Counterfactual Evaluation of Treatment Assignment Functions with Networked Observational Data—0
Counterfactual Explanation and Causal Inference in Service of Robustness in Robot Control—0
Counterfactual Explanation for Fairness in Recommendation—0
Counterfactual Fairness with Disentangled Causal Effect Variational Autoencoder—0
Counterfactual Fairness with Partially Known Causal Graph—0
Adaptive Doubly Robust Estimator from Non-stationary Logging Policy under a Convergence of Average Probability—0
Counterfactual Inference under Thompson Sampling—0
Counterfactual Invariance to Spurious Correlations: Why and How to Pass Stress Tests—0
Counterfactual Invariance to Spurious Correlations in Text Classification—0
Counterfactually Fair Regression with Double Machine Learning—0
Counterfactually Fair Reinforcement Learning via Sequential Data Preprocessing—0
Counterfactual Propagation for Semi-Supervised Individual Treatment Effect Estimation—0
Counterfactual Reasoning and Learning Systems—0
Counterfactual Representation Learning with Balancing Weights—0
Counterfactual Thinking for Long-tailed Information Extraction—0
Counting and Sampling from Markov Equivalent DAGs Using Clique Trees—0
Covariate-Balancing-Aware Interpretable Deep Learning models for Treatment Effect Estimation—0
Credit Ratings: Heterogeneous Effect on Capital Structure—0
Show:102550
← PrevPage 64 of 69Next →
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