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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 126–150 of 1722 papers

TitleStatusHype
Antibiotic Resistance Microbiology Dataset (ARMD): A De-identified Resource for Studying Antimicrobial Resistance Using Electronic Health Records—0
A Causal Inference Approach for Quantifying Research Impact—0
Riemannian Metric Learning: Closer to You than You Imagine—0
Black Box Causal Inference: Effect Estimation via Meta Prediction—0
Kernel-based estimators for functional causal effectsCode0
BotUmc: An Uncertainty-Aware Twitter Bot Detection with Multi-view Causal Inference—0
Learning Exposure Mapping Functions for Inferring Heterogeneous Peer Effects—0
Causal Inference on Outcomes Learned from Text—0
Learning Conditional Average Treatment Effects in Regression Discontinuity Designs using Bayesian Additive Regression Trees—0
Economic Causal Inference Based on DML Framework: Python Implementation of Binary and Continuous Treatment Variables—0
Transfer Learning in Latent Contextual Bandits with Covariate Shift Through Causal TransportabilityCode0
Semiparametric Triple Difference Estimators—0
Long-term Causal Inference via Modeling Sequential Latent Confounding—0
Nonparametric Heterogeneous Long-term Causal Effect Estimation via Data Combination—0
Revealing Treatment Non-Adherence Bias in Clinical Machine Learning Using Large Language Models—0
An Overview of Large Language Models for Statisticians—0
Joint Value Estimation and Bidding in Repeated First-Price Auctions—0
Practical programming research of Linear DML model based on the simplest Python code: From the standpoint of novice researchers—0
A novel approach to the relationships between data features -- based on comprehensive examination of mathematical, technological, and causal methodology—0
Time Series Treatment Effects Analysis with Always-Missing Controls—0
Causal Inference for Qualitative OutcomesCode1
Batch-Adaptive Annotations for Causal Inference with Complex-Embedded Outcomes—0
A Latent Causal Inference Framework for Ordinal VariablesCode0
Causal Analysis of ASR Errors for Children: Quantifying the Impact of Physiological, Cognitive, and Extrinsic Factors—0
Individualised Treatment Effects Estimation with Composite Treatments and Composite OutcomesCode0
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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