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 301–325 of 1722 papers

TitleStatusHype
A General Framework for Treatment Effect Estimation in Semi-Supervised and High Dimensional Settings—0
A Causal Adjustment Module for Debiasing Scene Graph Generation—0
Causal inference for the expected number of recurrent events in the presence of a terminal event—0
Causal Inference from Small High-dimensional Datasets—0
Causal Inference in Geosciences with Kernel Sensitivity Maps—0
Causal Inference on Investment Constraints and Non-stationarity in Dynamic Portfolio Optimization through Reinforcement Learning—0
A Systems Thinking Approach to Algorithmic Fairness—0
A scoping review of causal methods enabling predictions under hypothetical interventions—0
A General Causal Inference Framework for Cross-Sectional Observational Data—0
A Synthetic Business Cycle Approach to Counterfactual Analysis with Nonstationary Macroeconomic Data—0
Asymptotic Properties of the Distributional Synthetic Controls—0
A category theoretical argument for causal inference—0
Asymptotic Causal Inference—0
A Free Lunch with Influence Functions? Improving Neural Network Estimates with Concepts from Semiparametric Statistics—0
Causal Inference for Experiments with Latent Outcomes: Key Results and Their Implications for Design and Analysis—0
A Framework in CRM Customer Lifecycle: Identify Downward Trend and Potential Issues Detection—0
A Compositional Atlas for Algebraic Circuits—0
Causal Inference for Genomic Data with Multiple Heterogeneous Outcomes—0
A Survey on Causal Discovery: Theory and Practice—0
A Survey of Online Hate Speech through the Causal Lens—0
A Framework for Inferring Causality from Multi-Relational Observational Data using Conditional Independence—0
A Survey of Event Causality Identification: Principles, Taxonomy, Challenges, and Assessment—0
A Cautionary Tale on Integrating Studies with Disparate Outcome Measures for Causal Inference—0
Does Misclassifying Non-confounding Covariates as Confounders Affect the Causal Inference within the Potential Outcomes Framework?—0
A Forecaster's Review of Judea Pearl's Causality: Models, Reasoning and Inference, Second Edition, 2009—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