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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 1341–1350 of 1722 papers

Title	Date	Tasks	Status	Hype
Signal Processing on Directed Graphs	Aug 2, 2020	Causal Inference	—Unverified	0
Structural Causal Models Are (Solvable by) Credal Networks	Aug 2, 2020	Causal Inference	CodeCode Available	0
Determining the Relevance of Features for Deep Neural Networks	Aug 1, 2020	Causal Inference	—Unverified	0
Naïve regression requires weaker assumptions than factor models to adjust for multiple cause confounding	Jul 24, 2020	Causal Inferenceregression	—Unverified	0
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
Autoregressive flow-based causal discovery and inference	Jul 18, 2020	Causal DiscoveryCausal Inference	CodeCode Available	1
Latent Instrumental Variables as Priors in Causal Inference based on Independence of Cause and Mechanism	Jul 17, 2020	Causal DiscoveryCausal Inference	—Unverified	0
When deep learning meets causal inference: a computational framework for drug repurposing from real-world data	Jul 16, 2020	Causal Inference	CodeCode Available	1
Causal Inference using Gaussian Processes with Structured Latent Confounders	Jul 14, 2020	Causal InferenceGaussian Processes	—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