A new causal variance decomposition attributes observed variation in care delivery to eight components including sociodemographic modification of hospital effects, hospital access or selection, and their correlation.
Double/debiased machine learning for treatment and structural parameters.The Econometrics Journal, 21(1):C1–C68, 2018
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
representative citing papers
PI-CMDP combines causal backdoor identification under LOA, Markov state compression, and physics-guided doubly-robust estimation to achieve higher constraint repair success rates with fewer samples than baselines on the TPS benchmark.
Physical activity's protective association with lower mental distress strengthens monotonically with age and has eroded to null for young adults over the past decade.
citing papers explorer
-
Causal Variance Decompositions for Measuring Health Inequalities
A new causal variance decomposition attributes observed variation in care delivery to eight components including sociodemographic modification of hospital effects, hospital access or selection, and their correlation.
-
Physics-Informed Causal MDPs for Sequential Constraint Repair in Engineering Simulation Pipelines
PI-CMDP combines causal backdoor identification under LOA, Markov state compression, and physics-guided doubly-robust estimation to achieve higher constraint repair success rates with fewer samples than baselines on the TPS benchmark.
-
Age-Dependent Heterogeneity in the Association Between Physical Activity and Mental Distress: A Causal Machine Learning Analysis of 3.2 Million U.S. Adults
Physical activity's protective association with lower mental distress strengthens monotonically with age and has eroded to null for young adults over the past decade.