arXiv:2102.01199
공식 코드 · Papers with Code
Causal Inference with the Instrumental Variable Approach and Bayesian Nonparametric Machine Learning
원문 초록
We provide a new flexible framework for inference with the instrumental variable model. Rather than using linear specifications, functions characterizing the effects of instruments and other explanatory variables are estimated using machine learning via Bayesian Additive Regression Trees (BART). Error terms and their distribution are inferred using Dirichlet Process mixtures. Simulated and real examples show that when the true functions are linear, little is lost. But when nonlinearities are present, dramatic improvements are obtained with virtually no manual tuning.
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Causal Inference for Experiments with Latent Outcomes: Key Results and Their Implications for Design and Analysis
Debiased Ill-Posed Regression
Extraction of Heart Rate from PPG Signal: A Machine Learning Approach using Decision Tree Regression Algorithm
Using Data Mining to Predict Hospital Admissions From the Emergency Department
Active Learning for Manifold Gaussian Process Regression
Vector Contrastive Learning For Pixel-Wise Pretraining In Medical Vision
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