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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 1101–1110 of 1722 papers

Title	Date	Tasks	Status	Hype
A Primer on Deep Learning for Causal Inference	Oct 9, 2021	Causal InferenceDeep Learning	CodeCode Available	0
Many Proxy Controls	Oct 8, 2021	Causal Inference	—Unverified	0
Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP	Oct 7, 2021	Causal InferenceDomain Adaptation	CodeCode Available	0
Estimating Potential Outcome Distributions with Collaborating Causal Networks	Oct 4, 2021	Causal InferenceDecision Making	—Unverified	0
Effects of Multi-Aspect Online Reviews with Unobserved Confounders: Estimation and Implication	Oct 4, 2021	Causal Inference	CodeCode Available	0
Enhancing Model Robustness and Fairness with Causality: A Regularization Approach	Oct 3, 2021	Causal Inferencecounterfactual	CodeCode Available	0
Hierarchical Gaussian Process Models for Regression Discontinuity/Kink under Sharp and Fuzzy Designs	Oct 3, 2021	Causal Inferenceregression	—Unverified	0
Towards Principled Causal Effect Estimation by Deep Identifiable Models	Sep 30, 2021	Causal Inference	—Unverified	0
Mutual Information Minimization Based Disentangled Learning Framework For Causal Effect Estimation	Sep 29, 2021	Causal InferenceRepresentation Learning	—Unverified	0
Connecting Data to Mechanisms with Meta Structual Causal Model	Sep 29, 2021	Causal InferenceRepresentation Learning	—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