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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 1551–1560 of 1722 papers

Title	Date	Tasks	Status	Hype
Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and Beyond	Dec 30, 2019	BIG-bench Machine LearningCausal Inference	CodeCode Available	0
Local Linear Forests	Jul 30, 2018	Causal InferenceHeterogeneous Treatment Effect Estimation	CodeCode Available	0
An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls	Dec 25, 2017	Causal InferenceConformal Prediction	CodeCode Available	0
Evaluating shifts in mobility and COVID-19 case rates in U.S. counties: A demonstration of modified treatment policies for causal inference with continuous exposures	Oct 24, 2021	Causal Inference	CodeCode Available	0
Weighted Sum-Rate Maximization With Causal Inference for Latent Interference Estimation	Nov 15, 2022	Causal Inferencecounterfactual	CodeCode Available	0
A Semi-Synthetic Dataset Generation Framework for Causal Inference in Recommender Systems	Feb 23, 2022	Causal InferenceDataset Generation	CodeCode Available	0
Causality for Machine Learning	Nov 24, 2019	BIG-bench Machine LearningCausal Inference	CodeCode Available	0
Logic and Commonsense-Guided Temporal Knowledge Graph Completion	Nov 30, 2022	Causal InferenceKnowledge Graph Completion	CodeCode Available	0
Text-Transport: Toward Learning Causal Effects of Natural Language	Oct 31, 2023	AttributeCausal Inference	CodeCode Available	0
Bridging Nations: Quantifying the Role of Multilinguals in Communication on Social Media	Apr 7, 2023	Causal 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