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 951–1000 of 1722 papers

TitleStatusHype
What's the Harm? Sharp Bounds on the Fraction Negatively Affected by TreatmentCode0
Causal Inference from Small High-dimensional Datasets—0
Framework for inferring empirical causal graphs from binary data to support multidimensional poverty analysisCode0
SoK: Blockchain DecentralizationCode1
DagSim: Combining DAG-based model structure with unconstrained data types and relations for flexible, transparent, and modularized data simulationCode1
The interventional Bayesian Gaussian equivalent score for Bayesian causal inference with unknown soft interventionsCode0
XLTime: A Cross-Lingual Knowledge Transfer Framework for Temporal Expression ExtractionCode0
CaM-Gen: Causally Aware Metric-Guided Text Generation—0
Conceptualizing Treatment Leakage in Text-based Causal Inference—0
Causal Discovery on the Effect of Antipsychotic Drugs on Delirium Patients in the ICU using Large EHR Dataset—0
Partial Identification of Dose Responses with Hidden Confounders—0
Local Gaussian process extrapolation for BART models with applications to causal inference—0
An Efficient Approach for Optimizing the Cost-effective Individualized Treatment Rule Using Conditional Random ForestCode0
Adversarial Estimators—0
A unified theory of information transfer and causal relation—0
Interventional Multi-Instance Learning with Deconfounded Instance-Level Prediction—0
Causal Intervention for Subject-Deconfounded Facial Action Unit Recognition—0
Causal Disentanglement with Network Information for Debiased Recommendations—0
Statistical Perspective on Functional and Causal Neural Connectomics: The Time-Aware PC AlgorithmCode1
Causal Discovery and Causal Learning for Fire Resistance Evaluation: Incorporating Domain Knowledge—0
Machine Learning and Deep Learning -- A review for EcologistsCode0
Causality, Causal Discovery, and Causal Inference in Structural Engineering—0
CIRS: Bursting Filter Bubbles by Counterfactual Interactive Recommender SystemCode1
Equivariance Allows Handling Multiple Nuisance Variables When Analyzing Pooled Neuroimaging DatasetsCode0
SurvCaus : Representation Balancing for Survival Causal InferenceCode0
Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable DataCode0
From Intervention to Domain Transportation: A Novel Perspective to Optimize Recommendation—0
Tutorial: Modern Theoretical Tools for Understanding and Designing Next-generation Information Retrieval System—0
Robustness Against Weak or Invalid Instruments: Exploring Nonlinear Treatment Models with Machine LearningCode0
GCF: Generalized Causal Forest for Heterogeneous Treatment Effect Estimation in Online Marketplace—0
Deep Multi-Modal Structural Equations For Causal Effect Estimation With Unstructured Proxies—0
The worst of both worlds: A comparative analysis of errors in learning from data in psychology and machine learning—0
Identifiability of Causal-based Fairness Notions: A State of the Art—0
Multi-Task Adversarial Learning for Treatment Effect Estimation in Basket Trials—0
Effects of Epileptiform Activity on Discharge Outcome in Critically Ill Patients—0
Slangvolution: A Causal Analysis of Semantic Change and Frequency Dynamics in SlangCode0
Covariate-Balancing-Aware Interpretable Deep Learning models for Treatment Effect Estimation—0
Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventions—0
The Cost of Influence: How Gifts to Physicians Shape Prescriptions and Drug Costs—0
Neural Score Matching for High-Dimensional Causal InferenceCode0
Estimating causal effects with optimization-based methods: A review and empirical comparison—0
Off-Policy Evaluation with Policy-Dependent Optimization Response—0
Causal discovery for observational sciences using supervised machine learningCode0
Predicting the impact of treatments over time with uncertainty aware neural differential equationsCode0
A Semi-Synthetic Dataset Generation Framework for Causal Inference in Recommender SystemsCode0
Partial Identification with Noisy Covariates: A Robust Optimization Approach—0
Learning Infomax and Domain-Independent Representations for Causal Effect Inference with Real-World Data—0
Learning Causal Overhypotheses through Exploration in Children and Computational Models—0
A Free Lunch with Influence Functions? Improving Neural Network Estimates with Concepts from Semiparametric Statistics—0
Counterfactual Analysis of the Impact of the IMF Program on Child Poverty in the Global-South Region using Causal-Graphical Normalizing Flows—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