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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 1201–1250 of 1722 papers

TitleStatusHype
Weak Instrumental Variables: Limitations of Traditional 2SLS and Exploring Alternative Instrumental Variable Estimators—0
Quantum causal inference in the presence of hidden common causes: An entropic approach—0
Normalized multivariate time series causality analysis and causal graph reconstruction—0
Prospective Artificial Intelligence Approaches for Active Cyber Defence—0
Everything Has a Cause: Leveraging Causal Inference in Legal Text AnalysisCode1
Sequential Deconfounding for Causal Inference with Unobserved ConfoundersCode0
Double Robust Semi-Supervised Inference for the Mean: Selection Bias under MAR Labeling with Decaying OverlapCode0
Large-scale nonlinear Granger causality for inferring directed dependence from short multivariate time-series data—0
Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals with Application to Proximal Causal InferenceCode0
Multi-Source Causal Inference Using Control Variates—0
SUTD-TrafficQA: A Question Answering Benchmark and an Efficient Network for Video Reasoning over Traffic EventsCode1
User-Oriented Smart General AI System under Causal Inference—0
Causal Inference Under Unmeasured Confounding With Negative Controls: A Minimax Learning Approach—0
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges—0
SEMgraph: An R Package for Causal Network Analysis of High-Throughput Data with Structural Equation ModelsCode1
Time-uniform central limit theory and asymptotic confidence sequencesCode0
Estimating the causal effect of an intervention in a time series setting: the C-ARIMA approach—0
Combining Interventional and Observational Data Using Causal ReductionsCode0
Asymptotic Theory for IV-Based Reinforcement Learning with Potential Endogeneity—0
A Structural Causal Model for MR Images of Multiple SclerosisCode1
Oil and Gas Reservoirs Parameters Analysis Using Mixed Learning of Bayesian Networks—0
Confronting Machine Learning With Financial Research—0
Scalable Causal Domain AdaptationCode0
Online Multi-Armed Bandits with Adaptive Inference—0
Case Level Counterfactual Reasoning in Process Mining—0
A Review of Generalizability and Transportability—0
Quantum Entropic Causal Inference—0
SBI: A Simulation-Based Test of Identifiability for Bayesian Causal Inference—0
Towards Causal Representation Learning—0
Causal Mediation Analysis with Hidden Confounders—0
Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic ModelsCode0
Training a Resilient Q-Network against Observational InterferenceCode1
Adaptive Doubly Robust Estimator from Non-stationary Logging Policy under a Convergence of Average Probability—0
Causal Estimation with Functional Confounders—0
Do-calculus enables estimation of causal effects in partially observed biomolecular pathwaysCode0
A Critical Look at the Consistency of Causal Estimation With Deep Latent Variable Models—0
Selecting Treatment Effects Models for Domain Adaptation Using Causal Knowledge—0
Causal Inference for Time series Analysis: Problems, Methods and Evaluation—0
Estimating a Directed Tree for Extremes—0
Dynamic Structural Impact of the COVID-19 Outbreak on the Stock Market and the Exchange Rate: A Cross-country Analysis Among BRICS NationsCode0
Patterns, predictions, and actions: A story about machine learning—0
Generating Synthetic Text Data to Evaluate Causal Inference Methods—0
Grab the Reins of Crowds: Estimating the Effects of Crowd Movement Guidance Using Causal InferenceCode0
Causal versions of Maximum Entropy and Principle of Insufficient Reason—0
Integer Programming for Causal Structure Learning in the Presence of Latent VariablesCode0
Feedback in Imitation Learning: The Three Regimes of Covariate Shift—0
Policy Analysis using Synthetic Controls in Continuous-TimeCode0
Causal Inference with the Instrumental Variable Approach and Bayesian Nonparametric Machine LearningCode1
CDS -- Causal Inference with Deep Survival Model and Time-varying CovariatesCode1
Customer Price Sensitivities in Competitive Automobile Insurance Markets—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