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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 626–650 of 1722 papers

TitleStatusHype
The Adaptive Doubly Robust Estimator for Policy Evaluation in Adaptive Experiments and a Paradox Concerning Logging Policy—0
Causality for Natural Language Processing—0
Causality for Earth Science -- A Review on Time-series and Spatiotemporal Causality Methods—0
BayesIMP: Uncertainty Quantification for Causal Data Fusion—0
Analysing the Direction of Emotional Influence in Nonverbal Dyadic Communication: A Facial-Expression Study—0
Causality-Driven Audits of Model Robustness—0
Causality, Causal Discovery, and Causal Inference in Structural Engineering—0
Causality-Aware Neighborhood Methods for Recommender Systems—0
Causality and Explainability for Trustworthy Integrated Pest Management—0
Identification and Inference for Synthetic Control Methods with Spillover Effects: Estimating the Economic Cost of the Sudan Split—0
An Algorithmic Approach for Causal Health Equity: A Look at Race Differentials in Intensive Care Unit (ICU) Outcomes—0
A Causal Inference Approach for Quantifying Research Impact—0
Causality and Batch Reinforcement Learning: Complementary Approaches To Planning In Unknown Domains—0
Causality-Aided Trade-off Analysis for Machine Learning Fairness—0
Bayesian Nonparametric Causal Inference: Information Rates and Learning Algorithms—0
Causal Investigation of Public Opinion during the COVID-19 Pandemic via Social Media Text—0
Causal Interventions in Bond Multi-Dealer-to-Client Platforms—0
Bayesian Evolutionary Swarm Architecture: A Formal Epistemic System Grounded in Truth-Based Competition—0
Causal Intervention for Weakly-Supervised Semantic Segmentation—0
Causal Intervention for Subject-Deconfounded Facial Action Unit Recognition—0
Bayesian Discovery of Linear Acyclic Causal Models—0
Causal Interpretations in Observational Studies: The Role of Sociocultural Backgrounds and Team Dynamics—0
Bayesian Counterfactual Prediction Models for HIV Care Retention with Incomplete Outcome and Covariate Information—0
An AI-powered Bayesian generative modeling approach for causal inference in observational studies—0
Rolling with the Punches: Resilient Contrastive Pre-training under Non-Stationary Drift—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