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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 1581–1590 of 1722 papers

Title	Date	Tasks	Status	Hype
Causal Inference with Noisy and Missing Covariates via Matrix Factorization	Jun 3, 2018	Causal InferenceMatrix Completion	CodeCode Available	0
Practical Performative Policy Learning with Strategic Agents	Dec 2, 2024	Causal Inference	CodeCode Available	0
Sequential Deconfounding for Causal Inference with Unobserved Confounders	Apr 16, 2021	Causal InferenceDecision Making	CodeCode Available	0
Federated Causal Inference in Heterogeneous Observational Data	Jul 25, 2021	Causal Inference	CodeCode Available	0
Argumentative Causal Discovery	May 18, 2024	Causal DiscoveryCausal Inference	CodeCode Available	0
Causal Inference with Cocycles	May 22, 2024	Causal Inferencecounterfactual	CodeCode Available	0
Federated Causal Inference from Observational Data	Aug 24, 2023	Causal InferenceFederated Learning	CodeCode Available	0
Predicting the impact of treatments over time with uncertainty aware neural differential equations	Feb 24, 2022	Causal Inferencecounterfactual	CodeCode Available	0
Unveiling the Potential of Robustness in Selecting Conditional Average Treatment Effect Estimators	Feb 28, 2024	Causal Inferencecounterfactual	CodeCode Available	0
Finding Counterfactually Optimal Action Sequences in Continuous State Spaces	Jun 6, 2023	Causal InferenceDecision Making	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