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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 801–850 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
Collaborative Heterogeneous Causal Inference Beyond Meta-analysis—0
Categoroids: Universal Conditional Independence—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
Fast Restricted Causal Inference—0
Estimating causal effects with optimization-based methods: A review and empirical comparison—0
Estimating Causal Effects With Partial Covariates For Clinical Interpretability—0
Collaborative-controlled LASSO for Constructing Propensity Score-based Estimators in High-Dimensional Data—0
Collaborative causal inference on distributed data—0
Case Level Counterfactual Reasoning in Process Mining—0
Estimating Heterogeneous Causal Effects in the Presence of Irregular Assignment Mechanisms—0
An introduction to flexible methods for policy evaluation—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
A Causality-Based Learning Approach for Discovering the Underlying Dynamics of Complex Systems from Partial Observations with Stochastic Parameterization—0
Feature Selection as Causal Inference: Experiments with Text Classification—0
Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learning—0
Coarsened confounding for causal effects: a large-sample framework—0
Estimating Online Influence Needs Causal Modeling! Counterfactual Analysis of Social Media Engagement—0
ClusterSC: Advancing Synthetic Control with Donor Selection—0
Estimating the causal effect of an intervention in a time series setting: the C-ARIMA approach—0
Cascading Failure Prediction via Causal Inference—0
Estimating the treatment effect over time under general interference through deep learner integrated TMLE—0
Closing the loop on multisensory interactions: A neural architecture for multisensory causal inference and recalibration—0
Classifying Treatment Responders Under Causal Effect Monotonicity—0
Estimating Treatment Effects from Irregular Time Series Observations with Hidden Confounders—0
NESTER: An Adaptive Neurosymbolic Method for Causal Effect Estimation—0
Estimating Treatment Effects using Multiple Surrogates: The Role of the Surrogate Score and the Surrogate Index—0
Estimating Treatment Effects via Orthogonal Regularization—0
Causal Inference under Data Restrictions—0
Estimation Considerations in Contextual Bandits—0
Estimation of causal effects of multiple treatments in healthcare database studies with rare outcomes—0
Estimation of Treatment Effects in Extreme and Unobserved Data—0
Validating Causal Inference Methods—0
Evaluating Digital Agriculture Recommendations with Causal Inference—0
Evaluating Digital Tools for Sustainable Agriculture using Causal Inference—0
Evaluating Fairness Metrics in the Presence of Dataset Bias—0
Evaluating Interventional Reasoning Capabilities of Large Language Models—0
Causal Inference Under Unmeasured Confounding With Negative Controls: A Minimax Learning Approach—0
Causal Inference in medicine and in health policy, a summary—0
Evaluation Methods and Measures for Causal Learning Algorithms—0
Causal Fairness Assessment of Treatment Allocation with Electronic Health Records—0
An Introduction to Causal Discovery—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