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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 1371–1380 of 1722 papers

Title	Date	Tasks	Status	Hype
Computational Causal Inference	Jul 21, 2020	Causal InferenceDecision Making	—Unverified	0
Moment-Matching Graph-Networks for Causal Inference	Jul 20, 2020	Causal InferenceGraph Neural Network	CodeCode Available	0
Latent Instrumental Variables as Priors in Causal Inference based on Independence of Cause and Mechanism	Jul 17, 2020	Causal DiscoveryCausal Inference	—Unverified	0
Robust Identifiability in Linear Structural Equation Models of Causal Inference	Jul 14, 2020	Causal Inferenceparameter estimation	—Unverified	0
Causal Inference using Gaussian Processes with Structured Latent Confounders	Jul 14, 2020	Causal InferenceGaussian Processes	—Unverified	0
Intelligent Credit Limit Management in Consumer Loans Based on Causal Inference	Jul 10, 2020	Causal InferenceManagement	—Unverified	0
Meta Learning for Causal Direction	Jul 6, 2020	Causal InferenceMeta-Learning	—Unverified	0
Assessing External Validity Over Worst-case Subpopulations	Jul 5, 2020	Causal Inferencevalid	CodeCode Available	0
Learning to search efficiently for causally near-optimal treatments	Jul 2, 2020	Causal InferenceReinforcement Learning (RL)	CodeCode Available	0
A Perspective on Gaussian Processes for Earth Observation	Jul 2, 2020	Causal InferenceEarth Observation	—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