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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 Added Most Hyped Most Active Needs Verification Most Verified

Showing 451–460 of 1722 papers

Title	Date	Tasks	Status	Hype
Causal Inference with Treatment Measurement Error: A Nonparametric Instrumental Variable Approach	Jun 18, 2022	Causal Inference	—Unverified	0
Unveiling Environmental Sensitivity of Individual Gains in Influence Maximization	Jan 28, 2023	Causal InferenceSensitivity	—Unverified	0
Causal Information Bottleneck Boosts Adversarial Robustness of Deep Neural Network	Oct 25, 2022	Adversarial RobustnessCausal Inference	—Unverified	0
Rolling with the Punches: Resilient Contrastive Pre-training under Non-Stationary Drift	Feb 11, 2025	Causal InferenceContrastive Learning	—Unverified	0
A Primer on Causality in Data Science	Sep 7, 2018	Causal Inference	—Unverified	0
Causal Disentanglement with Network Information for Debiased Recommendations	Apr 14, 2022	Causal InferenceDisentanglement	—Unverified	0
Causal Intervention for Subject-Deconfounded Facial Action Unit Recognition	Apr 17, 2022	Causal InferenceFacial Action Unit Detection	—Unverified	0
Causal Intervention for Weakly-Supervised Semantic Segmentation	Sep 26, 2020	AttributeCausal Inference	—Unverified	0
Causal Interventions in Bond Multi-Dealer-to-Client Platforms	Jun 22, 2025	Causal Inference	—Unverified	0
Adversarial Estimators	Apr 22, 2022	Causal InferenceEconometrics	—Unverified	0

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All datasets IHDP Jobs IDHP

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Random Forest	Average Treatment Effect Error	0.96	—	Unverified
2	Balancing Linear Regression	Average Treatment Effect Error	0.93	—	Unverified
3	k-NN	Average Treatment Effect Error	0.79	—	Unverified
4	CEVAE	Average Treatment Effect Error	0.46	—	Unverified
5	Balancing Neural Network	Average Treatment Effect Error	0.42	—	Unverified
6	Causal Forest	Average Treatment Effect Error	0.4	—	Unverified
7	BCAUS DR	Average Treatment Effect Error	0.29	—	Unverified
8	TARNet	Average Treatment Effect Error	0.28	—	Unverified
9	Counterfactual Regression + WASS	Average Treatment Effect Error	0.27	—	Unverified
10	MTDL-KNN	Average Treatment Effect Error	0.23	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CFR WASS	Average Treatment Effect on the Treated Error	0.09	—	Unverified
2	CFR MMD	Average Treatment Effect on the Treated Error	0.08	—	Unverified
3	BART	Average Treatment Effect on the Treated Error	0.08	—	Unverified
4	GANITE	Average Treatment Effect on the Treated Error	0.06	—	Unverified
5	BCAUSS	Average Treatment Effect on the Treated Error	0.05	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BART	Average Treatment Effect Error	0.34	—	Unverified
2	OLS with separate regressors for each treatment	Average Treatment Effect Error	0.31	—	Unverified
3		Average Treatment Effect Error	-0.23	—	Unverified