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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 AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 251–275 of 1722 papers

TitleStatusHype
LLMScan: Causal Scan for LLM Misbehavior DetectionCode0
Federated Causal Inference: Multi-Study ATE Estimation beyond Meta-Analysis—0
Are Bayesian networks typically faithful?—0
Accounting for Missing Covariates in Heterogeneous Treatment Estimation—0
Differentially Private Covariate Balancing Causal Inference—0
A Novel Method to Metigate Demographic and Expert Bias in ICD Coding with Causal Inference—0
Counterfactual Generative Modeling with Variational Causal InferenceCode1
Do LLMs Have the Generalization Ability in Conducting Causal Inference?Code0
A Practical Approach to Causal Inference over TimeCode0
DAG-aware Transformer for Causal Effect EstimationCode0
Learning Representations of Instruments for Partial Identification of Treatment EffectsCode0
DiffPO: A causal diffusion model for learning distributions of potential outcomesCode1
Multimodal Clickbait Detection by De-confounding Biases Using Causal Representation Inference—0
Causal Image Modeling for Efficient Visual UnderstandingCode1
Medical Image Quality Assessment based on Probability of Necessity and Sufficiency—0
Counterfactual Causal Inference in Natural Language with Large Language ModelsCode1
Neural Networks Decoded: Targeted and Robust Analysis of Neural Network Decisions via Causal Explanations and Reasoning—0
Mitigating Modality Prior-Induced Hallucinations in Multimodal Large Language Models via Deciphering Attention CausalityCode2
Facial Action Unit Detection by Adaptively Constraining Self-Attention and Causally Deconfounding SampleCode0
Causal Inference Tools for a Better Evaluation of Machine Learning—0
Smaller Confidence Intervals From IPW Estimators via Data-Dependent Coarsening—0
Causal Representation Learning with Generative Artificial Intelligence: Application to Texts as Treatments—0
Interpretable, multi-dimensional Evaluation Framework for Causal Discovery from observational i.i.d. DataCode0
Using Deep Autoregressive Models as Causal Inference Engines—0
Detecting and Measuring Confounding Using Causal Mechanism ShiftsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Random ForestAverage Treatment Effect Error0.96—Unverified
2Balancing Linear RegressionAverage Treatment Effect Error0.93—Unverified
3k-NNAverage Treatment Effect Error0.79—Unverified
4CEVAEAverage Treatment Effect Error0.46—Unverified
5Balancing Neural NetworkAverage Treatment Effect Error0.42—Unverified
6Causal ForestAverage Treatment Effect Error0.4—Unverified
7BCAUS DRAverage Treatment Effect Error0.29—Unverified
8TARNetAverage Treatment Effect Error0.28—Unverified
9Counterfactual Regression + WASSAverage Treatment Effect Error0.27—Unverified
10MTDL-KNNAverage Treatment Effect Error0.23—Unverified
#ModelMetricClaimedVerifiedStatus
1CFR WASSAverage Treatment Effect on the Treated Error0.09—Unverified
2CFR MMDAverage Treatment Effect on the Treated Error0.08—Unverified
3BARTAverage Treatment Effect on the Treated Error0.08—Unverified
4GANITEAverage Treatment Effect on the Treated Error0.06—Unverified
5BCAUSSAverage Treatment Effect on the Treated Error0.05—Unverified
#ModelMetricClaimedVerifiedStatus
1BARTAverage Treatment Effect Error0.34—Unverified
2OLS with separate regressors for each treatmentAverage Treatment Effect Error0.31—Unverified
3Average Treatment Effect Error-0.23—Unverified