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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 21–30 of 1722 papers

Title	Date	Tasks	Status	Hype
Towards Robust Multimodal Emotion Recognition under Missing Modalities and Distribution Shifts	Jun 12, 2025	Causal Inferencecounterfactual	CodeCode Available	1
DeCaFlow: A Deconfounding Causal Generative Model	Mar 19, 2025	Causal Inferencecounterfactual	CodeCode Available	1
Causal Inference for Qualitative Outcomes	Feb 17, 2025	Causal Inference	CodeCode Available	1
REX: Causal Discovery based on Machine Learning and Explainability techniques	Jan 22, 2025	Causal DiscoveryCausal Inference	CodeCode Available	1
MECD+: Unlocking Event-Level Causal Graph Discovery for Video Reasoning	Jan 13, 2025	Causal DiscoveryCausal Inference	CodeCode Available	1
Eliciting Causal Abilities in Large Language Models for Reasoning Tasks	Dec 19, 2024	Causal Inference	CodeCode Available	1
Exploring Multi-Modal Data with Tool-Augmented LLM Agents for Precise Causal Discovery	Dec 18, 2024	Causal DiscoveryCausal Inference	CodeCode Available	1
CausalMob: Causal Human Mobility Prediction with LLMs-derived Human Intentions toward Public Events	Dec 3, 2024	ArticlesCausal Inference	CodeCode Available	1
Language Models as Causal Effect Generators	Nov 12, 2024	Causal Inferencecounterfactual	CodeCode Available	1
Language Agents Meet Causality -- Bridging LLMs and Causal World Models	Oct 25, 2024	Causal InferenceCommon Sense Reasoning	CodeCode Available	1

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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