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 201–250 of 1722 papers

TitleStatusHype
Causal Deep Learning—0
Adaptive Multi-Source Causal Inference—0
Correlation vs causation in Alzheimer's disease: an interpretability-driven study—0
Causal Decision Trees—0
Causal Deep Reinforcement Learning Using Observational Data—0
Causal Discovery and Counterfactual Explanations for Personalized Student Learning—0
Analyzing Behaviors of Mixed Traffic via Reinforcement Learning at Unsignalized Intersections—0
Analysis of cause-effect inference by comparing regression errors—0
The Adaptive Doubly Robust Estimator for Policy Evaluation in Adaptive Experiments and a Paradox Concerning Logging Policy—0
A Multi-class Ride-hailing Service Subsidy System Utilizing Deep Causal Networks—0
Identification and Inference for Synthetic Control Methods with Spillover Effects: Estimating the Economic Cost of the Sudan Split—0
A Causal Inference Approach for Quantifying Research Impact—0
Causal DAG extraction from a library of books or videos/movies—0
An AI-powered Bayesian generative modeling approach for causal inference in observational studies—0
Bayesian Counterfactual Prediction Models for HIV Care Retention with Incomplete Outcome and Covariate Information—0
Causal Coordinated Concurrent Reinforcement Learning—0
A Causal Framework for Precision Rehabilitation—0
A Bayesian Model for Bivariate Causal Inference—0
A Mixing Time Lower Bound for a Simplified Version of BART—0
Causal Contrastive Learning for Counterfactual Regression Over Time—0
Causal-Copilot: An Autonomous Causal Analysis Agent—0
Causal DAG Summarization (Full Version)—0
Causal Discovery from Conditionally Stationary Time Series—0
Bayesian Causal Inference in Doubly Gaussian DAG-probit Models—0
Bayesian causal inference via probabilistic program synthesis—0
Active and Passive Causal Inference Learning—0
Bayesian Causal Forests for Multivariate Outcomes: Application to Irish Data From an International Large Scale Education Assessment—0
Bayesian Discovery of Linear Acyclic Causal Models—0
Bayesian Evolutionary Swarm Architecture: A Formal Epistemic System Grounded in Truth-Based Competition—0
Bayesian Nonparametric Causal Inference: Information Rates and Learning Algorithms—0
Batch-Adaptive Annotations for Causal Inference with Complex-Embedded Outcomes—0
An Algorithmic Approach for Causal Health Equity: A Look at Race Differentials in Intensive Care Unit (ICU) Outcomes—0
BayesIMP: Uncertainty Quantification for Causal Data Fusion—0
Analysing the Direction of Emotional Influence in Nonverbal Dyadic Communication: A Facial-Expression Study—0
Be Causal: De-biasing Social Network Confounding in Recommendation—0
Behavioral Causal Inference—0
Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation—0
Benchmarking Causal Study to Interpret Large Language Models for Source Code—0
An Automated Approach to Causal Inference in Discrete Settings—0
Benign-Overfitting in Conditional Average Treatment Effect Prediction with Linear Regression—0
-Intact-VAE: Identifying and Estimating Causal Effects under Limited Overlap—0
Better Decisions through the Right Causal World Model—0
Beyond Bell's Theorem II: Scenarios with arbitrary causal structure—0
Beyond Correlation: Towards Causal Large Language Model Agents in Biomedicine—0
Beyond Flatland: A Geometric Take on Matching Methods for Treatment Effect Estimation—0
Beyond Patterns: Harnessing Causal Logic for Autonomous Driving Trajectory Prediction—0
Action needed to make carbon offsets from tropical forest conservation work for climate change mitigation—0
Local Projections Inference with High-Dimensional Covariates without Sparsity—0
An Empirical Comparison of Weak-IV-Robust Procedures in Just-Identified Models—0
A Computational Framework for Solving Nonlinear Binary OptimizationProblems in Robust Causal Inference—0
Show:102550
← PrevPage 5 of 35Next →
All datasetsIHDPJobsIDHP

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