Probabilistic Prediction for Binary Treatment Choice: with focus on personalized medicine
This paper extends my research applying statistical decision theory to treatment choice with sample data, using maximum regret to evaluate the performance of treatment rules. The specific new contribution is to study as-if optimization using estimates of illness probabilities in clinical choice between surveillance and aggressive treatment. Beyond its specifics, the paper sends a broad message. Statisticians and computer scientists have addressed conditional prediction for decision making in indirect ways, the former applying classical statistical theory and the latter measuring prediction accuracy in test samples. Neither approach is satisfactory. Statistical decision theory provides a coherent, generally applicable methodology.
Code (0)
등록된 구현이 없습니다.
Tasks
Decision MakingPredictionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Bandwidth Selection for Treatment Choice with Binary Outcomes
This study considers the treatment choice problem when outcome variables are binary. We focus on statistical treatment rules that plug in fitted values based on nonparametric kernel regression and show that optimizing tw…
regressionCognitive Noise and Altruistic Preferences
I study altruistic choices through the lens of a cognitively noisy decision-maker. I introduce a theoretical framework that demonstrates how increased cognitive noise can directionally affect altruistic decisions and put…
MathBayesian implementation of Targeted Maximum Likelihood Estimation for uncertainty quantification in causal effect estimation
Robust decision making involves making decisions in the presence of uncertainty and is often used in critical domains such as healthcare, supply chains, and finance. Causality plays a crucial role in decision-making as i…
Decision MakingTreatment Effects with Multidimensional Unobserved Heterogeneity: Identification of the Marginal Treatment Effect
This paper establishes sufficient conditions for the identification of the marginal treatment effects with multivalued treatments. Our model is based on a multinomial choice model with utility maximization. Our MTE gener…
Multiple-choiceProbabilistic Variational Causal Approach in Observational Studies
In this paper, we introduce a new causal methodology that accounts for the rarity and frequency of events in observational studies based on their relevance to the underlying problem. Specifically, we propose a direct cau…
counterfactual