SOTAVerified

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 676–700 of 1722 papers

TitleStatusHype
Epistemic Limits of Empirical Finance: Causal Reductionism and Self-Reference—0
A Neural Framework for Generalized Causal Sensitivity AnalysisCode0
Analyzing Behaviors of Mixed Traffic via Reinforcement Learning at Unsignalized Intersections—0
Designing monitoring strategies for deployed machine learning algorithms: navigating performativity through a causal lens—0
A causal intervention framework for synthesizing mobility data and evaluating predictive neural networks—0
Complementary Advantages of ChatGPTs and Human Readers in Reasoning: Evidence from English Text Reading Comprehension—0
Heteroskedasticity as a Signature of Association for Age-Related Genes—0
Causal prediction models for medication safety monitoring: The diagnosis of vancomycin-induced acute kidney injuryCode0
An adaptive denoising recommendation algorithm for causal separation biasCode0
The Impact of Generative Artificial Intelligence on Market Equilibrium: Evidence from a Natural Experiment—0
Do large language models and humans have similar behaviors in causal inference with script knowledge?Code0
Omitted Labels Induce Nontransitive Paradoxes in Causality—0
Causal Inference from Text: Unveiling Interactions between VariablesCode0
An Experimental Design for Anytime-Valid Causal Inference on Multi-Armed BanditsCode0
Causal Inference on Investment Constraints and Non-stationarity in Dynamic Portfolio Optimization through Reinforcement Learning—0
Learned Causal Method Prediction—0
High Precision Causal Model Evaluation with Conditional Randomization—0
Causal inference with Machine Learning-Based Covariate Representation—0
Structured Neural Networks for Density Estimation and Causal Inference—0
TRIALSCOPE: A Unifying Causal Framework for Scaling Real-World Evidence Generation with Biomedical Language Models—0
Variable Selection in Maximum Mean Discrepancy for Interpretable Distribution Comparison—0
Text-Transport: Toward Learning Causal Effects of Natural LanguageCode0
Generator Identification for Linear SDEs with Additive and Multiplicative Noise—0
Causal Q-Aggregation for CATE Model Selection—0
Local Discovery by Partitioning: Polynomial-Time Causal Discovery Around Exposure-Outcome PairsCode0
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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