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 801–825 of 1722 papers

TitleStatusHype
Entrywise Inference for Missing Panel Data: A Simple and Instance-Optimal Approach—0
Auction Throttling and Causal Inference of Online Advertising Effects—0
Epistemic Limits of Empirical Finance: Causal Reductionism and Self-Reference—0
Causal Inference of Script Knowledge—0
Error Causal inference for Multi-Fusion models—0
Establishing Causal Relationship Between Whole Slide Image Predictions and Diagnostic Evidence Subregions in Deep Learning—0
Estimating and Mitigating the Congestion Effect of Curbside Pick-ups and Drop-offs: A Causal Inference Approach—0
Causal Inference on Investment Constraints and Non-stationarity in Dynamic Portfolio Optimization through Reinforcement Learning—0
Estimating Causal Effects in Partially Directed Parametric Causal Factor Graphs—0
Causal Inference on Multivariate and Mixed-Type Data—0
Estimating Causal Effects of Text Interventions Leveraging LLMs—0
Collaborative Heterogeneous Causal Inference Beyond Meta-analysis—0
Estimating causal effects with optimization-based methods: A review and empirical comparison—0
Estimating Causal Effects With Partial Covariates For Clinical Interpretability—0
Categoroids: Universal Conditional Independence—0
Estimating Conditional Average Treatment Effects via Sufficient Representation Learning—0
Evaluating Digital Agriculture Recommendations with Causal Inference—0
Estimating Heterogeneous Causal Effects in the Presence of Irregular Assignment Mechanisms—0
Collaborative-controlled LASSO for Constructing Propensity Score-based Estimators in High-Dimensional Data—0
Estimating Heterogeneous Treatment Effects with Item-Level Outcome Data: Insights from Item Response Theory—0
Estimating Heterogenous Treatment Effects for Survival Data with Doubly Doubly Robust Estimator—0
Collaborative causal inference on distributed data—0
Case Level Counterfactual Reasoning in Process Mining—0
Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learning—0
An introduction to flexible methods for policy evaluation—0
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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