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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 541–550 of 1722 papers

Title	Date	Tasks	Status	Hype
The Impact of Generative Artificial Intelligence on Market Equilibrium: Evidence from a Natural Experiment	Nov 13, 2023	Causal Inference	—Unverified	0
Omitted Labels Induce Nontransitive Paradoxes in Causality	Nov 12, 2023	Causal Inference	—Unverified	0
Causal Inference from Text: Unveiling Interactions between Variables	Nov 9, 2023	Causal InferenceSelection bias	CodeCode Available	0
An Experimental Design for Anytime-Valid Causal Inference on Multi-Armed Bandits	Nov 9, 2023	Causal InferenceExperimental Design	CodeCode Available	0
Causal Inference on Investment Constraints and Non-stationarity in Dynamic Portfolio Optimization through Reinforcement Learning	Nov 8, 2023	Causal InferenceManagement	—Unverified	0
Learned Causal Method Prediction	Nov 7, 2023	Causal DiscoveryCausal Inference	—Unverified	0
CausalCite: A Causal Formulation of Paper Citations	Nov 5, 2023	Causal Inferencecounterfactual	CodeCode Available	1
Structured Neural Networks for Density Estimation and Causal Inference	Nov 3, 2023	Causal Inferencecounterfactual	—Unverified	0
Causal inference with Machine Learning-Based Covariate Representation	Nov 3, 2023	Causal Inference	—Unverified	0
High Precision Causal Model Evaluation with Conditional Randomization	Nov 3, 2023	Causal Inferencemodel	—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