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

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Showing 1451–1475 of 1722 papers

TitleStatusHype
Causal Inference Under Unmeasured Confounding With Negative Controls: A Minimax Learning Approach—0
Causal inference using deep neural networks—0
Causal Inference using Gaussian Processes with Structured Latent Confounders—0
Causal Inference from Slowly Varying Nonstationary Processes—0
Causal Inference Using LLM-Guided Discovery—0
Causal inference using the algorithmic Markov condition—0
Causal Inference Using Tractable Circuits—0
Causal inference via algebraic geometry: feasibility tests for functional causal structures with two binary observed variables—0
Causal Inference via Conditional Kolmogorov Complexity using MDL Binning—0
Causal Inference via Kernel Deviance Measures—0
Causal Inference via Nonlinear Variable Decorrelation for Healthcare Applications—0
Predictive Coding beyond Correlations—0
Causal inference with Bayes rule—0
Causal Inference with Complex Treatments: A Survey—0
Causal Inference with Conditional Front-Door Adjustment and Identifiable Variational Autoencoder—0
Causal Inference with Conditional Instruments using Deep Generative Models—0
Causal Inference with Corrupted Data: Measurement Error, Missing Values, Discretization, and Differential Privacy—0
Causal Inference with Deep Causal Graphs—0
Improving the Variance of Differentially Private Randomized Experiments through Clustering—0
Causal Inference with Double/Debiased Machine Learning for Evaluating the Health Effects of Multiple Mismeasured Pollutants—0
Causal inference with imperfect instrumental variables—0
Causal Inference with Large Language Model: A Survey—0
Causal Inference with Latent Variables: Recent Advances and Future Prospectives—0
Causal inference with Machine Learning-Based Covariate Representation—0
Causal Inference With Selectively Deconfounded Data—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