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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 1641–1650 of 1722 papers

Title	Date	Tasks	Status	Hype
Adversarial Generalized Method of Moments	Mar 19, 2018	Causal InferenceClustering	CodeCode Available	0
The Effect of Noise Level on Causal Identification with Additive Noise Models	Aug 24, 2021	Causal DiscoveryCausal Identification	CodeCode Available	0
Harmonization with Flow-based Causal Inference	Jun 12, 2021	Causal Inferencecounterfactual	CodeCode Available	0
HERMES: Hybrid Error-corrector Model with inclusion of External Signals for nonstationary fashion time series	Feb 7, 2022	Causal InferenceTime Series	CodeCode Available	0
Smoke and Mirrors in Causal Downstream Tasks	May 27, 2024	Causal InferenceRepresentation Learning	CodeCode Available	0
Heterogeneous causal effects with imperfect compliance: a Bayesian machine learning approach	May 29, 2019	BIG-bench Machine LearningCausal Inference	CodeCode Available	0
Rethinking recidivism through a causal lens	Nov 19, 2020	BIG-bench Machine LearningCausal Inference	CodeCode Available	0
Heterogeneous Peer Effects in the Linear Threshold Model	Jan 27, 2022	Causal Inferencemodel	CodeCode Available	0
MissDeepCausal: Causal Inference from Incomplete Data Using Deep Latent Variable Models	Feb 25, 2020	Causal InferenceImputation	CodeCode Available	0
Propensity Score Alignment of Unpaired Multimodal Data	Apr 2, 2024	Causal InferenceRepresentation Learning	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