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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 1601–1610 of 1722 papers

Title	Date	Tasks	Status	Hype
Framework for inferring empirical causal graphs from binary data to support multidimensional poverty analysis	May 12, 2022	Causal InferenceRelation	CodeCode Available	0
An Efficient Approach for Optimizing the Cost-effective Individualized Treatment Rule Using Conditional Random Forest	Apr 23, 2022	Causal Inference	CodeCode Available	0
Prescriptive Process Monitoring Under Resource Constraints: A Causal Inference Approach	Sep 7, 2021	Causal Inference	CodeCode Available	0
Matching with Text Data: An Experimental Evaluation of Methods for Matching Documents and of Measuring Match Quality	Jan 2, 2018	ArticlesCausal Inference	CodeCode Available	0
Variable Importance Matching for Causal Inference	Feb 23, 2023	Causal InferenceFeature Importance	CodeCode Available	0
A pragmatic approach to estimating average treatment effects from EHR data: the effect of prone positioning on mechanically ventilated COVID-19 patients	Sep 14, 2021	Causal Inference	CodeCode Available	0
Neural Network Parameter-optimization of Gaussian pmDAGs	Sep 25, 2023	Causal IdentificationCausal Inference	CodeCode Available	0
Preventing Spurious Interactions: A New Inductive Bias for Accurate Treatment Effect Estimation	May 28, 2025	Causal InferenceInductive Bias	CodeCode Available	0
Ancestral Causal Inference	Jun 22, 2016	Causal DiscoveryCausal Inference	CodeCode Available	0
Causal Inference Through the Structural Causal Marginal Problem	Feb 2, 2022	Causal Inferencecounterfactual	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