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 251–275 of 1722 papers

TitleStatusHype
Biased Stochastic First-Order Methods for Conditional Stochastic Optimization and Applications in Meta Learning—0
β-Intact-VAE: Identifying and Estimating Causal Effects under Limited Overlap—0
An End-to-End Framework to Identify Pathogenic Social Media Accounts on Twitter—0
Bivariate Causal Discovery and its Applications to Gene Expression and Imaging Data Analysis—0
A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation—0
Causal Effect Estimation: Recent Advances, Challenges, and Opportunities—0
Axiomatization of Interventional Probability Distributions—0
A Way to Synthetic Triple Difference—0
Algorithmic syntactic causal identification—0
Avoiding Biased Clinical Machine Learning Model Performance Estimates in the Presence of Label Selection—0
Average Controlled and Average Natural Micro Direct Effects in Summary Causal Graphs—0
A Critical Review of Causal Reasoning Benchmarks for Large Language Models—0
Causal Effect Estimation with Variational AutoEncoder and the Front Door Criterion—0
Algorithmic Bias in Recidivism Prediction: A Causal Perspective—0
A Causal Framework for Decomposing Spurious Variations—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 Disentanglement for Regulating Social Influence Bias in Social Recommendation—0
Automatic Debiased Machine Learning for Smooth Functionals of Nonparametric M-Estimands—0
Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition—0
ALCM: Autonomous LLM-Augmented Causal Discovery Framework—0
Automated hypothesis generation via Evolutionary Abduction—0
A Layered Architecture for Universal Causality—0
Acquiring and Generalizing Causal Inference Rules from Deverbal Noun Constructions—0
Causal Discovery with Stage Variables for Health Time Series—0
Show:102550
← PrevPage 11 of 69Next →
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