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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

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Showing 926–950 of 1722 papers

TitleStatusHype
Antibiotic-dependent instability of homeostatic plasticity for growth and environmental load—0
Direct-Effect Risk Minimization for Domain GeneralizationCode0
Causal Fairness Assessment of Treatment Allocation with Electronic Health Records—0
Applications of statistical causal inference in software engineering—0
Explainable Artificial Intelligence and Causal Inference based ATM Fraud Detection—0
On the Role of the Zero Conditional Mean Assumption for Causal Inference in Linear Models—0
Mitigating Frequency Bias in Next-Basket Recommendation via Deconfounders—0
Realization of Causal Representation Learning to Adjust Confounding Bias in Latent SpaceCode0
Weighted Sum-Rate Maximization With Causal Inference for Latent Interference EstimationCode0
Graph Neural Networks for Causal Inference Under Network Confounding—0
NESTER: An Adaptive Neurosymbolic Method for Causal Effect Estimation—0
Deep Causal Learning: Representation, Discovery and Inference—0
Evaluating Digital Tools for Sustainable Agriculture using Causal Inference—0
A Bayesian Semiparametric Method For Estimating Causal Quantile Effects—0
Propensity score models are better when post-calibratedCode0
Formalizing Statistical Causality via Modal Logic—0
Causal DAG extraction from a library of books or videos/movies—0
Spectral Representation Learning for Conditional Moment Models—0
A Survey on Causal Representation Learning and Future Work for Medical Image AnalysisCode0
Sample-Specific Root Causal Inference with Latent Variables—0
Causal Information Bottleneck Boosts Adversarial Robustness of Deep Neural Network—0
Dynamic Survival Transformers for Causal Inference with Electronic Health Records—0
LMPriors: Pre-Trained Language Models as Task-Specific Priors—0
Causally-guided Regularization of Graph Attention Improves Generalizability—0
Vision Paper: Causal Inference for Interpretable and Robust Machine Learning in Mobility Analysis—0
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All datasetsIHDPJobsIDHP

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