19 research outputs found
A Game Theoretic Setting of Capitation Versus Fee-For-Service Payment Systems
We aim to determine whether a game-theoretic model between an insurer and a
healthcare practice yields a predictive equilibrium that incentivizes either
player to deviate from a fee-for-service to capitation payment system. Using
United States data from various primary care surveys, we find that non-extreme
equilibria (i.e., shares of patients, or shares of patient visits, seen under a
fee-for-service payment system) can be derived from a Stackelberg game if
insurers award a non-linear bonus to practices based on performance. Overall,
both insurers and practices can be incentivized to embrace capitation payments
somewhat, but potentially at the expense of practice performance
Popular Support for Balancing Equity and Efficiency in Resource Allocation: A Case Study in Online Advertising to Increase Welfare Program Awareness
Algorithmically optimizing the provision of limited resources is commonplace
across domains from healthcare to lending. Optimization can lead to efficient
resource allocation, but, if deployed without additional scrutiny, can also
exacerbate inequality. Little is known about popular preferences regarding
acceptable efficiency-equity trade-offs, making it difficult to design
algorithms that are responsive to community needs and desires. Here we examine
this trade-off and concomitant preferences in the context of GetCalFresh, an
online service that streamlines the application process for California's
Supplementary Nutrition Assistance Program (SNAP, formerly known as food
stamps). GetCalFresh runs online advertisements to raise awareness of their
multilingual SNAP application service. We first demonstrate that when ads are
optimized to garner the most enrollments per dollar, a disproportionately small
number of Spanish speakers enroll due to relatively higher costs of non-English
language advertising. Embedding these results in a survey (N = 1,532) of a
diverse set of Americans, we find broad popular support for valuing equity in
addition to efficiency: respondents generally preferred reducing total
enrollments to facilitate increased enrollment of Spanish speakers. These
results buttress recent calls to reevaluate the efficiency-centric paradigm
popular in algorithmic resource allocation.Comment: This paper will be presented at the 2023 International Conference on
Web and Social Media (ICWSM'23
Federated Causal Inference in Heterogeneous Observational Data
We are interested in estimating the effect of a treatment applied to
individuals at multiple sites, where data is stored locally for each site. Due
to privacy constraints, individual-level data cannot be shared across sites;
the sites may also have heterogeneous populations and treatment assignment
mechanisms. Motivated by these considerations, we develop federated methods to
draw inference on the average treatment effects of combined data across sites.
Our methods first compute summary statistics locally using propensity scores
and then aggregate these statistics across sites to obtain point and variance
estimators of average treatment effects. We show that these estimators are
consistent and asymptotically normal. To achieve these asymptotic properties,
we find that the aggregation schemes need to account for the heterogeneity in
treatment assignments and in outcomes across sites. We demonstrate the validity
of our federated methods through a comparative study of two large medical
claims databases
Should I Stop or Should I Go: Early Stopping with Heterogeneous Populations
Randomized experiments often need to be stopped prematurely due to the
treatment having an unintended harmful effect. Existing methods that determine
when to stop an experiment early are typically applied to the data in aggregate
and do not account for treatment effect heterogeneity. In this paper, we study
the early stopping of experiments for harm on heterogeneous populations. We
first establish that current methods often fail to stop experiments when the
treatment harms a minority group of participants. We then use causal machine
learning to develop CLASH, the first broadly-applicable method for
heterogeneous early stopping. We demonstrate CLASH's performance on simulated
and real data and show that it yields effective early stopping for both
clinical trials and A/B tests.Comment: NeurIPS 2023 (spotlight
Potential for allocative harm in an environmental justice data tool
Neighborhood-level screening algorithms are increasingly being deployed to
inform policy decisions. We evaluate one such algorithm, CalEnviroScreen -
designed to promote environmental justice and used to guide hundreds of
millions of dollars in public funding annually - assessing its potential for
allocative harm. We observe the model to be sensitive to subjective model
decisions, with 16% of tracts potentially changing designation, as well as
financially consequential, estimating the effect of its positive designations
as a 104% (62-145%) increase in funding, equivalent to \$2.08 billion
(\$1.56-2.41 billion) over four years. We also observe allocative tradeoffs and
susceptibility to manipulation, raising ethical concerns. We recommend
incorporating sensitivity analyses to mitigate allocative harm and
accountability mechanisms to prevent misuse