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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 1–10 of 1722 papers

Title	Date	Tasks	Status	Hype
DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models	Jun 14, 2022	Causal Inference	CodeCode Available	5
Beyond Reward Hacking: Causal Rewards for Large Language Model Alignment	Jan 16, 2025	Causal Inferencecounterfactual	CodeCode Available	4
pgmpy: A Python Toolkit for Bayesian Networks	Apr 17, 2023	Causal DiscoveryCausal Identification	CodeCode Available	4
LinFusion: 1 GPU, 1 Minute, 16K Image	Sep 3, 2024	16kCausal Inference	CodeCode Available	3
CausalVLR: A Toolbox and Benchmark for Visual-Linguistic Causal Reasoning	Jun 30, 2023	Causal InferenceMedical Report Generation	CodeCode Available	3
DoWhy: An End-to-End Library for Causal Inference	Nov 9, 2020	Causal Inferencevalid	CodeCode Available	3
CausalML: Python Package for Causal Machine Learning	Feb 25, 2020	BIG-bench Machine LearningCausal Inference	CodeCode Available	3
Do we actually understand the impact of renewables on electricity prices? A causal inference approach	Jan 10, 2025	Causal Inference	CodeCode Available	2
A Comprehensive Guide to Explainable AI: From Classical Models to LLMs	Dec 1, 2024	Causal Inferencecounterfactual	CodeCode Available	2
Mitigating Modality Prior-Induced Hallucinations in Multimodal Large Language Models via Deciphering Attention Causality	Oct 7, 2024	Causal Inferencecounterfactual	CodeCode Available	2

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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