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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 681–690 of 1722 papers

Title	Date	Tasks	Status	Hype
Causality-Aided Trade-off Analysis for Machine Learning Fairness	May 22, 2023	Causal DiscoveryCausal Inference	—Unverified	0
The Decaying Missing-at-Random Framework: Model Doubly Robust Causal Inference with Partially Labeled Data	May 22, 2023	Causal InferencePartially Labeled Datasets	—Unverified	0
uCTRL: Unbiased Contrastive Representation Learning via Alignment and Uniformity for Collaborative Filtering	May 22, 2023	Causal InferenceCollaborative Filtering	CodeCode Available	0
Estimation Beyond Data Reweighting: Kernel Method of Moments	May 18, 2023	Causal Inference	CodeCode Available	0
Counterfactually Comparing Abstaining Classifiers	May 17, 2023	Causal Inferencecounterfactual	CodeCode Available	0
A Survey on Causal Discovery: Theory and Practice	May 17, 2023	Causal DiscoveryCausal Inference	—Unverified	0
The Impact of Missing Data on Causal Discovery: A Multicentric Clinical Study	May 17, 2023	Causal DiscoveryCausal Inference	—Unverified	0
The Hardness of Reasoning about Probabilities and Causality	May 16, 2023	Causal Inference	—Unverified	0
A Causal Inference Framework for Leveraging External Controls in Hybrid Trials	May 15, 2023	Causal Inference	—Unverified	0
Artificial intelligence to advance Earth observation: : A review of models, recent trends, and pathways forward	May 15, 2023	Causal InferenceEarth Observation	—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