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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 426–450 of 1722 papers

TitleStatusHype
Causal Order Discovery based on Monotonic SCMs—0
Cascading Failure Prediction via Causal Inference—0
Deoxys: A Causal Inference Engine for Unhealthy Node Mitigation in Large-scale Cloud Infrastructure—0
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
A Novel Method to Metigate Demographic and Expert Bias in ICD Coding with Causal Inference—0
Differentially Private Covariate Balancing Causal Inference—0
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
Medical Image Quality Assessment based on Probability of Necessity and Sufficiency—0
Multimodal Clickbait Detection by De-confounding Biases Using Causal Representation Inference—0
Neural Networks Decoded: Targeted and Robust Analysis of Neural Network Decisions via Causal Explanations and Reasoning—0
Causal Inference Tools for a Better Evaluation of Machine Learning—0
Facial Action Unit Detection by Adaptively Constraining Self-Attention and Causally Deconfounding SampleCode0
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
Potential Field as Scene Affordance for Behavior Change-Based Visual Risk Object Identification—0
Towards Representation Learning for Weighting Problems in Design-Based Causal InferenceCode0
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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