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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 1321–1330 of 1722 papers

Title	Date	Tasks	Status	Hype
Causal inference using deep neural networks	Nov 25, 2020	Causal Inference	—Unverified	0
Counterfactual Fairness with Disentangled Causal Effect Variational Autoencoder	Nov 24, 2020	AttributeCausal Inference	—Unverified	0
Rethinking recidivism through a causal lens	Nov 19, 2020	BIG-bench Machine LearningCausal Inference	CodeCode Available	0
A scoping review of causal methods enabling predictions under hypothetical interventions	Nov 19, 2020	Causal InferenceDecision Making	—Unverified	0
Confounding Feature Acquisition for Causal Effect Estimation	Nov 17, 2020	Causal Inference	CodeCode Available	0
Split-Treatment Analysis to Rank Heterogeneous Causal Effects for Prospective Interventions	Nov 11, 2020	Causal InferenceMarketing	—Unverified	0
Incorporating Causal Effects into Deep Learning Predictions on EHR Data	Nov 11, 2020	Causal InferenceDeep Learning	—Unverified	0
Causal Expectation-Maximisation	Nov 4, 2020	Causal Inferencecounterfactual	CodeCode Available	0
Doubly Robust Off-Policy Learning on Low-Dimensional Manifolds by Deep Neural Networks	Nov 3, 2020	Causal Inference	—Unverified	0
High-Dimensional Feature Selection for Sample Efficient Treatment Effect Estimation	Nov 3, 2020	Causal Inferencefeature selection	—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