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 901–950 of 1722 papers

TitleStatusHype
DESCN: Deep Entire Space Cross Networks for Individual Treatment Effect EstimationCode1
CA-SpaceNet: Counterfactual Analysis for 6D Pose Estimation in SpaceCode1
Functional Generalized Empirical Likelihood Estimation for Conditional Moment RestrictionsCode0
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
Open Vocabulary Object Detection with Proposal Mining and Prediction EqualizationCode1
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
Towards Understanding How Machines Can Learn Causal OverhypothesesCode1
Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential EquationsCode1
Large-Scale Differentiable Causal Discovery of Factor GraphsCode1
Interpretable Gait Recognition by Granger Causality—0
DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal modelsCode5
Estimating Causal Effects Under Image Confounding Bias with an Application to Poverty in AfricaCode1
Causal Inference-Based Root Cause Analysis for Online Service Systems with Intervention RecognitionCode1
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
Active Bayesian Causal InferenceCode1
Combinatorial Causal BanditsCode0
Discovering Ancestral Instrumental Variables for Causal Inference from Observational DataCode0
Prescriptive maintenance with causal machine learning—0
Revisiting the General Identifiability Problem—0
Learning Disentangled Representations for Counterfactual Regression via Mutual Information Minimization—0
Causal Investigation of Public Opinion during the COVID-19 Pandemic via Social Media Text—0
Causal Explanations for Sequential Decision Making Under Uncertainty—0
A Fundamental Probabilistic Fuzzy Logic Framework Suitable for Causal ReasoningCode0
Comparison of meta-learners for estimating multi-valued treatment heterogeneous effectsCode0
Detecting hidden confounding in observational data using multiple environmentsCode0
Counterfactual Fairness with Partially Known Causal Graph—0
CEBaB: Estimating the Causal Effects of Real-World Concepts on NLP Model BehaviorCode1
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
Identifying Patient-Specific Root Causes of DiseaseCode0
Causal Machine Learning for Healthcare and Precision Medicine—0
Neuroevolutionary Feature Representations for Causal Inference—0
A New Central Limit Theorem for the Augmented IPW Estimator: Variance Inflation, Cross-Fit Covariance and Beyond—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