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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 676–700 of 1722 papers

TitleStatusHype
Causal Inference with Conditional Front-Door Adjustment and Identifiable Variational Autoencoder—0
Causal Inference with Complex Treatments: A Survey—0
Axiomatization of Interventional Probability Distributions—0
Causal inference with Bayes rule—0
A Way to Synthetic Triple Difference—0
Predictive Coding beyond Correlations—0
Causal Inference via Nonlinear Variable Decorrelation for Healthcare Applications—0
Avoiding Biased Clinical Machine Learning Model Performance Estimates in the Presence of Label Selection—0
Causal Inference via Kernel Deviance Measures—0
Causal Inference via Conditional Kolmogorov Complexity using MDL Binning—0
Average Controlled and Average Natural Micro Direct Effects in Summary Causal Graphs—0
Algorithmic syntactic causal identification—0
A Critical Review of Causal Reasoning Benchmarks for Large Language Models—0
Causal inference via algebraic geometry: feasibility tests for functional causal structures with two binary observed variables—0
Causal Inference Using Tractable Circuits—0
Causal inference using the algorithmic Markov condition—0
Causal Inference Using LLM-Guided Discovery—0
Algorithmic Bias in Recidivism Prediction: A Causal Perspective—0
Causal Inference from Slowly Varying Nonstationary Processes—0
Causal Inference using Gaussian Processes with Structured Latent Confounders—0
Causal inference using deep neural networks—0
Automatic Double Reinforcement Learning in Semiparametric Markov Decision Processes with Applications to Long-Term Causal Inference—0
A Critical Look at the Consistency of Causal Estimation With Deep Latent Variable Models—0
Causal Inference Under Unmeasured Confounding With Negative Controls: A Minimax Learning Approach—0
Causal Inference under Networked Interference and Intervention Policy Enhancement—0
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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