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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 1381–1390 of 1722 papers

Title	Date	Tasks	Status	Hype	Score
Causal Effect Estimation: Recent Advances, Challenges, and Opportunities	Feb 2, 2023	Causal InferenceMarketing	—Unverified	0	0
Causal Effect Estimation with Variational AutoEncoder and the Front Door Criterion	Apr 24, 2023	Causal InferenceRepresentation Learning	—Unverified	0	0
Causal Effect Identification from Multiple Incomplete Data Sources: A General Search-based Approach	Feb 4, 2019	Causal IdentificationCausal Inference	—Unverified	0	0
Causal Effect Variational Autoencoder with Uniform Treatment	Nov 16, 2021	Causal InferenceDomain Adaptation	—Unverified	0	0
Causal Estimation with Functional Confounders	Feb 17, 2021	Causal Inference	—Unverified	0	0
Causal-Ex: Causal Graph-based Micro and Macro Expression Spotting	Mar 12, 2025	Causal Inference	—Unverified	0	0
Causal Feature Selection with Dimension Reduction for Interpretable Text Classification	Oct 9, 2020	Causal InferenceClassification	—Unverified	0	0
Causal Post-Processing of Predictive Models	Jun 13, 2024	Causal InferenceDecision Making	—Unverified	0	0
Causal Forecasting for Pricing	Dec 23, 2023	Causal InferenceDecision Making	—Unverified	0	0
Causal GNNs: A GNN-Driven Instrumental Variable Approach for Causal Inference in Networks	Sep 13, 2024	Causal Inference	—Unverified	0	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