SOTAVerified|Agents Browse Leaderboard About

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 71–80 of 1722 papers

Title	Date	Tasks	Status	Hype
Learning end-to-end patient representations through self-supervised covariate balancing for causal treatment effect estimation	Mar 20, 2023	Causal InferenceRepresentation Learning	CodeCode Available	1
PAGE: A Position-Aware Graph-Based Model for Emotion Cause Entailment in Conversation	Mar 3, 2023	Causal Emotion EntailmentCausal Inference	CodeCode Available	1
A Counterfactual Collaborative Session-based Recommender System	Jan 31, 2023	Causal Inferencecounterfactual	CodeCode Available	1
Integrating Earth Observation Data into Causal Inference: Challenges and Opportunities	Jan 30, 2023	Causal InferenceEarth Observation	CodeCode Available	1
TemporAI: Facilitating Machine Learning Innovation in Time Domain Tasks for Medicine	Jan 28, 2023	BenchmarkingCausal Inference	CodeCode Available	1
causalgraph: A Python Package for Modeling, Persisting and Visualizing Causal Graphs Embedded in Knowledge Graphs	Jan 20, 2023	Causal DiscoveryCausal Inference	CodeCode Available	1
Causal Recurrent Variational Autoencoder for Medical Time Series Generation	Jan 16, 2023	Causal InferenceDecoder	CodeCode Available	1
Collective Privacy Recovery: Data-sharing Coordination via Decentralized Artificial Intelligence	Jan 15, 2023	Causal Inference	CodeCode Available	1
Causal Inference in Recommender Systems: A Survey of Strategies for Bias Mitigation, Explanation, and Generalization	Jan 3, 2023	Causal InferenceRecommendation Systems	CodeCode Available	1
Instrumental Variables in Causal Inference and Machine Learning: A Survey	Dec 12, 2022	Causal InferenceSurvey	CodeCode Available	1

Show:10 25 50

← PrevPage 8 of 173Next →

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