SOTAVerified|Agents Browse Leaderboard About

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 1361–1370 of 1722 papers

Title	Date	Tasks	Status	Hype
Causal Inference in Possibly Nonlinear Factor Models	Aug 31, 2020	Causal Inferencecounterfactual	CodeCode Available	0
Path Dependent Structural Equation Models	Aug 24, 2020	Causal Inference	—Unverified	0
Hi-CI: Deep Causal Inference in High Dimensions	Aug 22, 2020	Causal Inferencecounterfactual	—Unverified	0
Estimation of causal effects of multiple treatments in healthcare database studies with rare outcomes	Aug 18, 2020	Causal Inferenceregression	—Unverified	0
Estimating heterogeneous survival treatment effect in observational data using machine learning	Aug 17, 2020	BIG-bench Machine LearningCausal Inference	CodeCode Available	0
Estimating Causal Effects with the Neural Autoregressive Density Estimator	Aug 17, 2020	Causal Inference	CodeCode Available	0
Structural Causal Models Are (Solvable by) Credal Networks	Aug 2, 2020	Causal Inference	CodeCode Available	0
Signal Processing on Directed Graphs	Aug 2, 2020	Causal Inference	—Unverified	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

Show:10 25 50

← PrevPage 137 of 173Next →

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