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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 551–575 of 1722 papers

TitleStatusHype
Antibiotic Resistance Microbiology Dataset (ARMD): A De-identified Resource for Studying Antimicrobial Resistance Using Electronic Health Records—0
CausalCellSegmenter: Causal Inference inspired Diversified Aggregation Convolution for Pathology Image Segmentation—0
Antibiotic-dependent instability of homeostatic plasticity for growth and environmental load—0
Advancing Causal Inference: A Nonparametric Approach to ATE and CATE Estimation with Continuous Treatments—0
A Bayesian Semiparametric Method For Estimating Causal Quantile Effects—0
Advancements in Recommender Systems: A Comprehensive Analysis Based on Data, Algorithms, and Evaluation—0
Answering Causal Queries at Layer 3 with DiscoSCMs-Embracing Heterogeneity—0
A Causally Formulated Hazard Ratio Estimation through Backdoor Adjustment on Structural Causal Model—0
Causal bootstrapping—0
CausalBERT: Injecting Causal Knowledge Into Pre-trained Models with Minimal Supervision—0
Answering Complex Causal Queries With the Maximum Causal Set Effect—0
Correcting invalid regression discontinuity designs with multiple time period data—0
Causal Bayesian Optimization—0
An R package for parametric estimation of causal effects—0
Causal Bandits: Learning Good Interventions via Causal Inference—0
Causal-Aware Intelligent QoE Optimization for VR Interaction with Adaptive Keyframe Extraction—0
An Overview of Large Language Models for Statisticians—0
Adjustment with Many Regressors Under Covariate-Adaptive Randomizations—0
Counterfactual Analysis of the Impact of the IMF Program on Child Poverty in the Global-South Region using Causal-Graphical Normalizing Flows—0
Counterfactual Fairness with Disentangled Causal Effect Variational Autoencoder—0
An Overview of Causal Inference using Kernel Embeddings—0
Adjustment Criteria for Recovering Causal Effects from Missing Data—0
Conceptualizing Treatment Leakage in Text-based Causal Inference—0
Conceptualizing Treatment Leakage in Text-based Causal Inference—0
CausalAPM: Generalizable Literal Disentanglement for NLU Debiasing—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