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Open Access Publications from the University of California

Scholarly Works (3 results)

  • Thesis
  • Peer Reviewed
UC Berkeley Electronic Theses and Dissertations (2021)

I consider the type of statistical experiment commonly referred to as adaptive trials, in which the experimenter interacts with an individual or a set of individuals, and, sequentially, over time steps $t=1,\ldots,T$, observes a vector of measurements $L_1(t)$ on the individual or individuals, then assigns treatment vector $A(t)$, and then observes a post-treatment vector of measurements $L_2(t)$. In an adaptive trial, the experimenter can update the treatment distribution at $t$ based on previous observations.

This very general formulation covers many common settings such as dynamic treatment regimes, the stochastic contextual bandit model, the Markov Decision Process model in reinforcement learning. I consider two related types of learning tasks: causal inference from data collected under an adaptive trial, and sequential decision making with the objective of either maximizing the sample efficiency for an estimation task, or of minimizing some form of cumulative regret.

My primary concerns are to develop statistical methods and algorithms that use statistical models that assume no more than is known from domain knowledge (and therefore are nonparametric), and that are as sample efficient as possible.

    Cover page: Statistical methods for causal inference fromsequentially collected data and sequential decisionmaking
    • Article
    • Peer Reviewed
    UC San Francisco Previously Published Works (2020)
    The identification of disease hotspots is an increasingly important public health problem. While geospatial modeling offers an opportunity to predict the locations of hotspots using suitable environmental and climatological data, little attention has been paid to optimizing the design of surveys used to inform such models. Here we introduce an adaptive sampling scheme optimized to identify hotspot locations where prevalence exceeds a relevant threshold. Our approach incorporates ideas from Bayesian optimization theory to adaptively select sample batches. We present an experimental simulation study based on survey data of schistosomiasis and lymphatic filariasis across four countries. Results across all scenarios explored show that adaptive sampling produces superior results and suggest that similar performance to random sampling can be achieved with a fraction of the sample size.
      • Article
      • Peer Reviewed
      UC San Francisco Previously Published Works (2020)
      The identification of disease hotspots is an increasingly important public health problem. While geospatial modeling offers an opportunity to predict the locations of hotspots using suitable environmental and climatological data, little attention has been paid to optimizing the design of surveys used to inform such models. Here we introduce an adaptive sampling scheme optimized to identify hotspot locations where prevalence exceeds a relevant threshold. Our approach incorporates ideas from Bayesian optimization theory to adaptively select sample batches. We present an experimental simulation study based on survey data of schistosomiasis and lymphatic filariasis across four countries. Results across all scenarios explored show that adaptive sampling produces superior results and suggest that similar performance to random sampling can be achieved with a fraction of the sample size.
        Cover page: Finding hotspots: development of an adaptive spatial sampling approach
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