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 1001–1050 of 1722 papers

TitleStatusHype
On the Representation of Causal Background Knowledge and its Applications in Causal Inference—0
On the Need and Applicability of Causality for Fairness: A Unified Framework for AI Auditing and Legal Analysis—0
On The Universality of Diagrams for Causal Inference and The Causal Reproducing Property—0
The Short-term Impact of Congestion Taxes on Ridesourcing Demand and Traffic Congestion: Evidence from Chicago—0
A Causal Approach for Business Optimization: Application on an Online Marketplace—0
Breaking Feedback Loops in Recommender Systems with Causal Inference—0
Non-Parametric Inference of Relational DependenceCode0
Treatment Effect Estimation with Observational Network Data using Machine LearningCode0
Instrumented Common Confounding—0
The Amenability Framework: Rethinking Causal Ordering Without Estimating Causal Effects—0
Intelligent Request Strategy Design in Recommender System—0
Measuring the Effect of Training Data on Deep Learning Predictions via Randomized ExperimentsCode0
Causal Inference with Treatment Measurement Error: A Nonparametric Instrumental Variable Approach—0
Embodied Scene-aware Human Pose Estimation—0
Reframed GES with a Neural Conditional Dependence MeasureCode0
Combinatorial Pure Exploration of Causal Bandits—0
Interpretable Gait Recognition by Granger Causality—0
A Simple Unified Approach to Testing High-Dimensional Conditional Independences for Categorical and Ordinal DataCode0
Confounder Analysis in Measuring Representation in Product Funnels—0
Estimating and Mitigating the Congestion Effect of Curbside Pick-ups and Drop-offs: A Causal Inference Approach—0
Discovering Ancestral Instrumental Variables for Causal Inference from Observational DataCode0
Combinatorial Causal BanditsCode0
Prescriptive maintenance with causal machine learning—0
Learning Disentangled Representations for Counterfactual Regression via Mutual Information Minimization—0
Revisiting the General Identifiability Problem—0
Causal Investigation of Public Opinion during the COVID-19 Pandemic via Social Media Text—0
A Fundamental Probabilistic Fuzzy Logic Framework Suitable for Causal ReasoningCode0
Causal Explanations for Sequential Decision Making Under Uncertainty—0
Comparison of meta-learners for estimating multi-valued treatment heterogeneous effectsCode0
Counterfactual Fairness with Partially Known Causal Graph—0
Detecting hidden confounding in observational data using multiple environmentsCode0
Pessimism in the Face of Confounders: Provably Efficient Offline Reinforcement Learning in Partially Observable Markov Decision Processes—0
Leveraging Causal Inference for Explainable Automatic Program Repair—0
Identifying Patient-Specific Root Causes with the Heteroscedastic Noise Model—0
Causal Machine Learning for Healthcare and Precision Medicine—0
Identifying Patient-Specific Root Causes of DiseaseCode0
Neuroevolutionary Feature Representations for Causal Inference—0
What's the Harm? Sharp Bounds on the Fraction Negatively Affected by TreatmentCode0
A New Central Limit Theorem for the Augmented IPW Estimator: Variance Inflation, Cross-Fit Covariance and Beyond—0
Causal Inference from Small High-dimensional Datasets—0
Framework for inferring empirical causal graphs from binary data to support multidimensional poverty analysisCode0
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
An Efficient Approach for Optimizing the Cost-effective Individualized Treatment Rule Using Conditional Random ForestCode0
Local Gaussian process extrapolation for BART models with applications to causal inference—0
Adversarial Estimators—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