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

Showing 12011225 of 1722 papers

TitleStatusHype
Adjustment Criteria for Recovering Causal Effects from Missing Data0
Adjustment with Many Regressors Under Covariate-Adaptive Randomizations0
Advancements in Recommender Systems: A Comprehensive Analysis Based on Data, Algorithms, and Evaluation0
Advancing Causal Inference: A Nonparametric Approach to ATE and CATE Estimation with Continuous Treatments0
Adventurer: Optimizing Vision Mamba Architecture Designs for Efficiency0
Adventures in Demand Analysis Using AI0
Adversarial Estimators0
Adversarial Orthogonal Regression: Two non-Linear Regressions for Causal Inference0
AFA-PredNet: The action modulation within predictive coding0
A fast PC algorithm for high dimensional causal discovery with multi-core PCs0
Affirmative Algorithms: The Legal Grounds for Fairness as Awareness0
Nested Nonparametric Instrumental Variable Regression0
A Forecaster's Review of Judea Pearl's Causality: Models, Reasoning and Inference, Second Edition, 20090
A Framework for Inferring Causality from Multi-Relational Observational Data using Conditional Independence0
A Framework in CRM Customer Lifecycle: Identify Downward Trend and Potential Issues Detection0
A Free Lunch with Influence Functions? Improving Neural Network Estimates with Concepts from Semiparametric Statistics0
A General Causal Inference Framework for Cross-Sectional Observational Data0
A General Framework for Treatment Effect Estimation in Semi-Supervised and High Dimensional Settings0
A Graphical Approach to State Variable Selection in Off-policy Learning0
A Hamiltonian Higher-Order Elasticity Framework for Dynamic Diagnostics(2HOED)0
AI Assurance using Causal Inference: Application to Public Policy0
A Layered Architecture for Universal Causality0
ALCM: Autonomous LLM-Augmented Causal Discovery Framework0
Algorithmic Bias in Recidivism Prediction: A Causal Perspective0
Algorithmic syntactic causal identification0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Random ForestAverage Treatment Effect Error0.96Unverified
2Balancing Linear RegressionAverage Treatment Effect Error0.93Unverified
3k-NNAverage Treatment Effect Error0.79Unverified
4CEVAEAverage Treatment Effect Error0.46Unverified
5Balancing Neural NetworkAverage Treatment Effect Error0.42Unverified
6Causal ForestAverage Treatment Effect Error0.4Unverified
7BCAUS DRAverage Treatment Effect Error0.29Unverified
8TARNetAverage Treatment Effect Error0.28Unverified
9Counterfactual Regression + WASSAverage Treatment Effect Error0.27Unverified
10MTDL-KNNAverage Treatment Effect Error0.23Unverified
#ModelMetricClaimedVerifiedStatus
1CFR WASSAverage Treatment Effect on the Treated Error0.09Unverified
2CFR MMDAverage Treatment Effect on the Treated Error0.08Unverified
3BARTAverage Treatment Effect on the Treated Error0.08Unverified
4GANITEAverage Treatment Effect on the Treated Error0.06Unverified
5BCAUSSAverage Treatment Effect on the Treated Error0.05Unverified
#ModelMetricClaimedVerifiedStatus
1BARTAverage Treatment Effect Error0.34Unverified
2OLS with separate regressors for each treatmentAverage Treatment Effect Error0.31Unverified
3Average Treatment Effect Error-0.23Unverified