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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 1681–1690 of 1722 papers

Title	Date	Tasks	Status	Hype
Removing systematic errors for exoplanet search via latent causes	May 12, 2015	AstronomyCausal Inference	CodeCode Available	0
Distinguishing Cause from Effect Based on Exogeneity	Apr 22, 2015	Causal Inference	—Unverified	0
Telling cause from effect in deterministic linear dynamical systems	Mar 4, 2015	Causal DiscoveryCausal Inference	—Unverified	0
Towards a Learning Theory of Cause-Effect Inference	Feb 9, 2015	Causal InferenceGeneralization Bounds	CodeCode Available	0
A fast PC algorithm for high dimensional causal discovery with multi-core PCs	Feb 9, 2015	Causal DiscoveryCausal Inference	—Unverified	0
Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations	Jan 27, 2015	Causal InferenceProbabilistic Programming	—Unverified	0
Long-term causal effects via behavioral game theory	Jan 10, 2015	Causal Inference	—Unverified	0
From dependency to causality: a machine learning approach	Dec 19, 2014	BIG-bench Machine LearningCausal Inference	—Unverified	0
Distinguishing cause from effect using observational data: methods and benchmarks	Dec 11, 2014	Causal DiscoveryCausal Inference	—Unverified	0
Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components	Nov 14, 2014	Causal InferenceTime Series	—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