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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 1621–1630 of 1722 papers

Title	Date	Tasks	Status	Hype
Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions	Jul 20, 2017	Causal InferenceDomain Adaptation	CodeCode Available	0
The Inflation Technique Completely Solves the Causal Compatibility Problem	Jul 20, 2017	Causal Inference	—Unverified	0
FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference	Jul 19, 2017	Causal InferenceManagement	—Unverified	0
On Adaptive Propensity Score Truncation in Causal Inference	Jul 18, 2017	Causal Inference	CodeCode Available	0
Learning linear structural equation models in polynomial time and sample complexity	Jul 15, 2017	Causal InferenceComputational Efficiency	—Unverified	0
Fast Restricted Causal Inference	Jul 13, 2017	Causal Inference	—Unverified	0
Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition	Jul 9, 2017	Causal Inference	—Unverified	0
Collaborative-controlled LASSO for Constructing Propensity Score-based Estimators in High-Dimensional Data	Jun 30, 2017	Causal InferenceModel Selection	—Unverified	0
Restricted Causal Inference Algorithm	Jun 30, 2017	Causal Inference	—Unverified	0
Deep Counterfactual Networks with Propensity-Dropout	Jun 19, 2017	Causal Inferencecounterfactual	CodeCode Available	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