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 1226–1250 of 1722 papers

TitleStatusHype
A Computational Framework for Solving Nonlinear Binary OptimizationProblems in Robust Causal Inference—0
A Mixing Time Lower Bound for a Simplified Version of BART—0
An AI-powered Bayesian generative modeling approach for causal inference in observational studies—0
An Algorithmic Approach for Causal Health Equity: A Look at Race Differentials in Intensive Care Unit (ICU) Outcomes—0
Analysing the Direction of Emotional Influence in Nonverbal Dyadic Communication: A Facial-Expression Study—0
Analysis of cause-effect inference by comparing regression errors—0
Analyzing Behaviors of Mixed Traffic via Reinforcement Learning at Unsignalized Intersections—0
An Automated Approach to Causal Inference in Discrete Settings—0
Ancestral Instrument Method for Causal Inference without Complete Knowledge—0
An Efficient Doubly-Robust Test for the Kernel Treatment Effect—0
An Empirical Comparison of Weak-IV-Robust Procedures in Just-Identified Models—0
An End-to-End Framework for Marketing Effectiveness Optimization under Budget Constraint—0
An End-to-End Framework to Identify Pathogenic Social Media Accounts on Twitter—0
An evaluation framework for comparing causal inference models—0
A New Central Limit Theorem for the Augmented IPW Estimator: Variance Inflation, Cross-Fit Covariance and Beyond—0
An Explainable AI Approach to Large Language Model Assisted Causal Model Auditing and Development—0
An Introduction to Causal Discovery—0
Causal Inference in medicine and in health policy, a summary—0
An introduction to flexible methods for policy evaluation—0
A novel approach to the relationships between data features -- based on comprehensive examination of mathematical, technological, and causal methodology—0
A Novel Method to Metigate Demographic and Expert Bias in ICD Coding with Causal Inference—0
A Novel Two-level Causal Inference Framework for On-road Vehicle Quality Issues Diagnosis—0
An Overview of Causal Inference using Kernel Embeddings—0
An Overview of Large Language Models for Statisticians—0
An R package for parametric estimation of causal effects—0
Show:102550
← PrevPage 50 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