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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 1591–1600 of 1722 papers

Title	Date	Tasks	Status	Hype
Adversarial Generalized Method of Moments	Mar 19, 2018	Causal InferenceClustering	CodeCode Available	0
Deep Learning for Causal Inference	Mar 1, 2018	Causal InferenceDeep Learning	—Unverified	0
A Framework in CRM Customer Lifecycle: Identify Downward Trend and Potential Issues Detection	Feb 25, 2018	Causal InferenceManagement	—Unverified	0
Learning Weighted Representations for Generalization Across Designs	Feb 23, 2018	Causal InferenceDomain Adaptation	—Unverified	0
Analysis of cause-effect inference by comparing regression errors	Feb 19, 2018	Causal Inferenceregression	—Unverified	0
Closing the loop on multisensory interactions: A neural architecture for multisensory causal inference and recalibration	Feb 19, 2018	Causal Inference	—Unverified	0
DeepMatch: Balancing Deep Covariate Representations for Causal Inference Using Adversarial Training	Feb 15, 2018	Causal Inference	—Unverified	0
Benchmarking Framework for Performance-Evaluation of Causal Inference Analysis	Feb 14, 2018	BenchmarkingCausal Inference	CodeCode Available	0
Counting and Sampling from Markov Equivalent DAGs Using Clique Trees	Feb 5, 2018	Causal Inference	—Unverified	0
Distinguishing Cause from Effect Using Quantiles: Bivariate Quantile Causal Discovery	Jan 31, 2018	Causal DiscoveryCausal Inference	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