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 851–900 of 1722 papers

TitleStatusHype
Distilling Causal Effect from Miscellaneous Other-Class for Continual Named Entity RecognitionCode1
LLMEffiChecker: Understanding and Testing Efficiency Degradation of Large Language ModelsCode0
FAST: Improving Controllability for Text Generation with Feedback Aware Self-Training—0
Text-driven Video Prediction—0
Causal Inference for Chatting HandoffCode1
Transfer Learning for Individual Treatment Effect Estimation—0
Predicting Cellular Responses with Variational Causal Inference and Refined Relational InformationCode1
Causal Inference via Nonlinear Variable Decorrelation for Healthcare Applications—0
Causal inference in drug discovery and development—0
Causal Proxy Models for Concept-Based Model ExplanationsCode1
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
A Survey of Deep Causal Models and Their Industrial ApplicationsCode1
Quantifying How Hateful Communities Radicalize Online Users—0
Avoiding Biased Clinical Machine Learning Model Performance Estimates in the Presence of Label Selection—0
ρ-GNF: A Copula-based Sensitivity Analysis to Unobserved Confounding Using Normalizing FlowsCode0
Stochastic Tree Ensembles for Estimating Heterogeneous Effects—0
Unifying Causal Inference and Reinforcement Learning using Higher-Order Category Theory—0
Normalizing Flows for Interventional Density EstimationCode0
Variational Causal InferenceCode1
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
Causal Inference in Recommender Systems: A Survey and Future DirectionsCode1
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
Causal Effect Identification in Uncertain Causal Networks—0
Long-term Causal Effects Estimation via Latent Surrogates Representation LearningCode0
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
Deep Reinforcement Learning for Multi-Agent InteractionCode2
Tangential Wasserstein ProjectionsCode0
Cross-Modal Causal Relational Reasoning for Event-Level Visual Question AnsweringCode1
Counterfactual Reasoning for Out-of-distribution Multimodal Sentiment AnalysisCode1
Building Human Values into Recommender Systems: An Interdisciplinary Synthesis—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