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 1551–1600 of 1722 papers

TitleStatusHype
Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and BeyondCode0
Local Linear ForestsCode0
An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic ControlsCode0
Evaluating shifts in mobility and COVID-19 case rates in U.S. counties: A demonstration of modified treatment policies for causal inference with continuous exposuresCode0
Weighted Sum-Rate Maximization With Causal Inference for Latent Interference EstimationCode0
A Semi-Synthetic Dataset Generation Framework for Causal Inference in Recommender SystemsCode0
Causality for Machine LearningCode0
Logic and Commonsense-Guided Temporal Knowledge Graph CompletionCode0
Text-Transport: Toward Learning Causal Effects of Natural LanguageCode0
Bridging Nations: Quantifying the Role of Multilinguals in Communication on Social MediaCode0
Explainable Federated Bayesian Causal Inference and Its Application in Advanced ManufacturingCode0
Long-term Causal Effects Estimation via Latent Surrogates Representation LearningCode0
Poincare: Recommending Publication Venues via Treatment Effect EstimationCode0
Long-term Causal Inference Under Persistent Confounding via Data CombinationCode0
Explaining Text Classifiers with Counterfactual RepresentationsCode0
Brain-Inspired Visual Odometry: Balancing Speed and Interpretability through a System of Systems ApproachCode0
Causality-enhanced Decision-Making for Autonomous Mobile Robots in Dynamic EnvironmentsCode0
Semi-supervised Anomaly Detection via Adaptive Reinforcement Learning-Enabled Method with Causal Inference for Sensor SignalsCode0
Machine Learning and Deep Learning -- A review for EcologistsCode0
Machine learning for causal inference: on the use of cross-fit estimatorsCode0
Extrapolating Treatment Effects in Multi-Cutoff Regression Discontinuity DesignsCode0
Facial Action Unit Detection by Adaptively Constraining Self-Attention and Causally Deconfounding SampleCode0
Policy Analysis using Synthetic Controls in Continuous-TimeCode0
Peer-induced Fairness: A Causal Approach for Algorithmic Fairness AuditingCode0
BiomedSQL: Text-to-SQL for Scientific Reasoning on Biomedical Knowledge BasesCode0
Machine learning in policy evaluation: new tools for causal inferenceCode0
Semi-supervised learning and the question of true versus estimated propensity scoresCode0
A neural network oracle for quantum nonlocality problems in networksCode0
A Neural Framework for Generalized Causal Sensitivity AnalysisCode0
The Blessings of Multiple CausesCode0
Causal Inference with Noisy and Missing Covariates via Matrix FactorizationCode0
Practical Performative Policy Learning with Strategic AgentsCode0
Sequential Deconfounding for Causal Inference with Unobserved ConfoundersCode0
Federated Causal Inference in Heterogeneous Observational DataCode0
Argumentative Causal DiscoveryCode0
Causal Inference with CocyclesCode0
Federated Causal Inference from Observational DataCode0
Predicting the impact of treatments over time with uncertainty aware neural differential equationsCode0
Unveiling the Potential of Robustness in Selecting Conditional Average Treatment Effect EstimatorsCode0
Finding Counterfactually Optimal Action Sequences in Continuous State SpacesCode0
Finding Regions of Heterogeneity in Decision-Making via Expected Conditional CovarianceCode0
Finding Valid Adjustments under Non-ignorability with Minimal DAG KnowledgeCode0
Marginal Density Ratio for Off-Policy Evaluation in Contextual BanditsCode0
Predictive Performance Comparison of Decision Policies Under ConfoundingCode0
On Causal Inference with Model-Based OutcomesCode0
Bayesian nonparametric discontinuity designCode0
Forecasting Algorithms for Causal Inference with Panel DataCode0
Bias and high-dimensional adjustment in observational studies of peer effectsCode0
Benchmarking Framework for Performance-Evaluation of Causal Inference AnalysisCode0
A flexible Bayesian g-formula for causal survival analyses with time-dependent confoundingCode0
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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