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

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Showing 1151–1200 of 1722 papers

TitleStatusHype
Exposing Disparities in Flood Adaptation for Equitable Future Interventions—0
Extracting Physical Causality from Measurements to Detect and Localize False Data Injection Attacks—0
FAIR: A Causal Framework for Accurately Inferring Judgments Reversals—0
fairadapt: Causal Reasoning for Fair Data Pre-processing—0
Fair Effect Attribution in Parallel Online Experiments—0
Fast Causal Inference with Non-Random Missingness by Test-Wise Deletion—0
FAST: Improving Controllability for Text Generation with Feedback Aware Self-Training—0
Fast Restricted Causal Inference—0
Feature Selection as Causal Inference: Experiments with Text Classification—0
A Two-Stage Feature Selection Approach for Robust Evaluation of Treatment Effects in High-Dimensional Observational Data—0
Federated Causal Inference from Multi-Site Observational Data via Propensity Score Aggregation—0
Federated Causal Inference in Healthcare: Methods, Challenges, and Applications—0
Federated Causal Inference: Multi-Study ATE Estimation beyond Meta-Analysis—0
Feedback Detection for Live Predictors—0
Feedback in Imitation Learning: The Three Regimes of Covariate Shift—0
Fixed-Population Causal Inference for Models of Equilibrium—0
FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference—0
Forests for Differences: Robust Causal Inference Beyond Parametric DiD—0
Formalizing Statistical Causality via Modal Logic—0
Foundation Models for Causal Inference via Prior-Data Fitted Networks—0
From Correlation to Causation: Understanding Climate Change through Causal Analysis and LLM Interpretations—0
From Dependence to Causation—0
From dependency to causality: a machine learning approach—0
From Intervention to Domain Transportation: A Novel Perspective to Optimize Recommendation—0
From Prompts to Constructs: A Dual-Validity Framework for LLM Research in Psychology—0
From Text to Treatment Effects: A Meta-Learning Approach to Handling Text-Based Confounding—0
From What Ifs to Insights: Counterfactuals in Causal Inference vs. Explainable AI—0
GCF: Generalized Causal Forest for Heterogeneous Treatment Effect Estimation in Online Marketplace—0
Generalization bound for estimating causal effects from observational network data—0
Generalized Kernel Ridge Regression for Causal Inference with Missing-at-Random Sample Selection—0
Kernel methods for long term dose response curves—0
Generalized Optimal Matching Methods for Causal Inference—0
General Transportability of Soft Interventions: Completeness Results—0
Generating High-Fidelity Privacy-Conscious Synthetic Patient Data for Causal Effect Estimation with Multiple Treatments—0
Generating Synthetic Text Data to Evaluate Causal Inference Methods—0
Generative Intervention Models for Causal Perturbation Modeling—0
Generator Identification for Linear SDEs with Additive and Multiplicative Noise—0
Geometry-Aware Normalizing Wasserstein Flows for Optimal Causal Inference—0
GP CaKe: Effective brain connectivity with causal kernels—0
Granger Causality for Compressively Sensed Sparse Signals—0
Graph Disentangle Causal Model: Enhancing Causal Inference in Networked Observational Data—0
Graph Infomax Adversarial Learning for Treatment Effect Estimation with Networked Observational Data—0
Graph Neural Networks: Theory for Estimation with Application on Network Heterogeneity—0
G-Transformer: Counterfactual Outcome Prediction under Dynamic and Time-varying Treatment Regimes—0
Guiding Treatment Strategies: The Role of Adjuvant Anti-Her2 Neu Therapy and Skin/Nipple Involvement in Local Recurrence-Free Survival in Breast Cancer Patients—0
Half-AVAE: Adversarial-Enhanced Factorized and Structured Encoder-Free VAE for Underdetermined Independent Component Analysis—0
Half-VAE: An Encoder-Free VAE to Bypass Explicit Inverse Mapping—0
Estimating Heterogeneous Causal Effect of Polysubstance Usage on Drug Overdose from Large-Scale Electronic Health Record—0
Heterogeneous Effects of Software Patches in a Multiplayer Online Battle Arena Game—0
Heterogeneous Treatment Effect Estimation using machine learning for Healthcare application: tutorial and benchmark—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