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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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 401–450 of 1722 papers

TitleStatusHype
Causal Inference of Script Knowledge—0
Causal Inference on Discrete Data via Estimating Distance Correlations—0
Causal Inference on Investment Constraints and Non-stationarity in Dynamic Portfolio Optimization through Reinforcement Learning—0
Causal Inference on Multivariate and Mixed-Type Data—0
Causal Inference on Outcomes Learned from Text—0
Causal Inference on Time Series using Restricted Structural Equation Models—0
Causal Estimation with Functional Confounders—0
AFA-PredNet: The action modulation within predictive coding—0
Accounting for Missing Covariates in Heterogeneous Treatment Estimation—0
Real-Time Anomaly Detection with Synthetic Anomaly Monitoring (SAM)—0
Causal Effect Variational Autoencoder with Uniform Treatment—0
Causal Inference through a Witness Protection Program—0
A Review of Generalizability and Transportability—0
Estimation and Inference on Nonlinear and Heterogeneous Effects—0
Causal Effect Identification from Multiple Incomplete Data Sources: A General Search-based Approach—0
Causal Effect Estimation with Variational AutoEncoder and the Front Door Criterion—0
Causal Inference Under Approximate Neighborhood Interference—0
Causal Inference under Data Restrictions—0
Causal Inference Under Interference And Network Uncertainty—0
Causal Inference under Interference through Designed Markets—0
Causal Inference under Networked Interference and Intervention Policy Enhancement—0
Causal Inference Under Unmeasured Confounding With Negative Controls: A Minimax Learning Approach—0
A rational model of causal inference with continuous causes—0
Causal inference using deep neural networks—0
Causal Inference using Gaussian Processes with Structured Latent Confounders—0
Causal Inference from Slowly Varying Nonstationary Processes—0
Causal Inference Using LLM-Guided Discovery—0
Causal inference using the algorithmic Markov condition—0
Adversarial Orthogonal Regression: Two non-Linear Regressions for Causal Inference—0
Causal inference via algebraic geometry: feasibility tests for functional causal structures with two binary observed variables—0
Causally-guided Regularization of Graph Attention Improves Generalizability—0
A primer on optimal transport for causal inference with observational data—0
Causal Inference via Nonlinear Variable Decorrelation for Healthcare Applications—0
Predictive Coding beyond Correlations—0
Causal inference with Bayes rule—0
A Way to Synthetic Triple Difference—0
Causal Inference with Complex Treatments: A Survey—0
Causal Inference with Conditional Front-Door Adjustment and Identifiable Variational Autoencoder—0
Causal Inference with Conditional Instruments using Deep Generative Models—0
Causal Inference with Corrupted Data: Measurement Error, Missing Values, Discretization, and Differential Privacy—0
Causal Inference with Deep Causal Graphs—0
Improving the Variance of Differentially Private Randomized Experiments through Clustering—0
Causal Inference with Double/Debiased Machine Learning for Evaluating the Health Effects of Multiple Mismeasured Pollutants—0
Causal inference with imperfect instrumental variables—0
Causal Inference with Large Language Model: A Survey—0
Causal Inference with Latent Variables: Recent Advances and Future Prospectives—0
Causal Effect Estimation: Recent Advances, Challenges, and Opportunities—0
Causally-Aware Intraoperative Imputation for Overall Survival Time Prediction—0
Causal Inference With Selectively Deconfounded Data—0
A Primer on Causality in Data Science—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