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 951–1000 of 1722 papers

TitleStatusHype
A Mixing Time Lower Bound for a Simplified Version of BART—0
Fair Effect Attribution in Parallel Online Experiments—0
Distributionally Robust Causal Inference with Observational Data—0
On the Identifiability and Estimation of Causal Location-Scale Noise ModelsCode0
Developing a general-purpose clinical language inference model from a large corpus of clinical notes—0
Sample Constrained Treatment Effect EstimationCode0
Disentangling Causal Effects from Sets of Interventions in the Presence of Unobserved ConfoundersCode0
Causal and Counterfactual Views of Missing Data Models—0
DeepMed: Semiparametric Causal Mediation Analysis with Debiased Deep LearningCode0
LLMEffiChecker: Understanding and Testing Efficiency Degradation of Large Language ModelsCode0
Text-driven Video Prediction—0
FAST: Improving Controllability for Text Generation with Feedback Aware Self-Training—0
Transfer Learning for Individual Treatment Effect Estimation—0
Causal Inference via Nonlinear Variable Decorrelation for Healthcare Applications—0
Causal inference in drug discovery and development—0
Minimax Optimal Kernel Operator Learning via Multilevel Training—0
Off-policy estimation of linear functionals: Non-asymptotic theory for semi-parametric efficiency—0
Weather2vec: Representation Learning for Causal Inference with Non-Local Confounding in Air Pollution and Climate StudiesCode0
Granger Causality for Compressively Sensed Sparse Signals—0
Off-Policy Evaluation for Episodic Partially Observable Markov Decision Processes under Non-Parametric Models—0
Quantifying How Hateful Communities Radicalize Online Users—0
ρ-GNF: A Copula-based Sensitivity Analysis to Unobserved Confounding Using Normalizing FlowsCode0
Stochastic Tree Ensembles for Estimating Heterogeneous Effects—0
Avoiding Biased Clinical Machine Learning Model Performance Estimates in the Presence of Label Selection—0
Normalizing Flows for Interventional Density EstimationCode0
Unifying Causal Inference and Reinforcement Learning using Higher-Order Category Theory—0
Smiles in Profiles: Improving Efficiency While Reducing Disparities in Online Marketplaces—0
The Impact of the #MeToo Movement on Language at Court -- A text-based causal inference approach—0
An evaluation framework for comparing causal inference models—0
Data-Driven Influence Functions for Optimization-Based Causal Inference—0
Virtual Control Group: Measuring Hidden Performance Metrics—0
Categoroids: Universal Conditional Independence—0
Meta-Causal Feature Learning for Out-of-Distribution Generalization—0
Twin Papers: A Simple Framework of Causal Inference for Citations via CouplingCode0
Application of Causal Inference to Analytical Customer Relationship Management in Banking and Insurance—0
A Causality-Based Learning Approach for Discovering the Underlying Dynamics of Complex Systems from Partial Observations with Stochastic Parameterization—0
Network inference via process motifs for lagged correlation in linear stochastic processes—0
Inference on Strongly Identified Functionals of Weakly Identified Functions—0
Collaborative causal inference on distributed data—0
Contrastive Counterfactual Learning for Causality-aware Interpretable Recommender Systems—0
Valid Inference After Causal Discovery—0
Long-term Causal Effects Estimation via Latent Surrogates Representation LearningCode0
Causal Effect Identification in Uncertain Causal Networks—0
Recovering the Graph Underlying Networked Dynamical Systems under Partial Observability: A Deep Learning ApproachCode0
Forecasting Algorithms for Causal Inference with Panel DataCode0
Causality in cardiorespiratory signals in pediatric cardiac patients—0
The Face of Affective Disorders—0
Tangential Wasserstein ProjectionsCode0
Building Human Values into Recommender Systems: An Interdisciplinary Synthesis—0
Functional Generalized Empirical Likelihood Estimation for Conditional Moment RestrictionsCode0
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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