These papers are working drafts of research which often appear in final form in academic journals. The published versions may differ from the working versions provided here.
SSRN Research Paper Series
The Social Science Research Network’s Research Paper Series includes working papers produced by Stanford GSB the Rock Center.
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Computationally efficient contextual bandits are often based on estimating a predictive model of rewards given contexts and arms using past data. However, when the reward model is not well-specified, the bandit algorithm may incur…
In medicine, randomized clinical trials are the gold standard for informing treatment decisions. Observational comparative effectiveness research is often plagued by selection bias, and expert-selected covariates may not be…
We study how fathers’ access to workplace flexibility affects maternal postpartum health. We use variation from a Swedish reform that granted new fathers more flexibility to take intermittent parental leave during the postpartum…
When the government commits to a debt policy, the future value of government primary surpluses at all horizons is dictated by the debt dynamics under the risk-neutral measure. We compare the present discounted value of future…
The health care system commonly relies on information about family medical history in the allocation of screenings and in diagnostic processes. At the same time, an emerging literature documents that treatment for “marginally…
We study the dynamics of participation and health care consumption in the Affordable Care Act’s health insurance marketplaces. Unlike other health insurance contexts, we find individuals commonly drop coverage midyear — roughly 30…
Choice screen auctions have been recently deployed in 31 European countries, allowing consumers to choose their preferred search engine on Google’s Android platform instead of automatically defaulting them to Google’s own search…
Tractable contextual bandit algorithms often rely on the realizability assumption — i.e., that the true expected reward model belongs to a known class, such as linear functions. We investigate issues that arise in the absence of…
Alongside the outbreak of the novel coronavirus, an “infodemic” of myths and hoax cures is spreading over online media outlets and social media platforms. Building on the literature on combating fake news, we evaluate experimental…
In many areas, practitioners seek to use observational data to learn a treatment assignment policy that satisfies application-specific constraints, such as budget, fairness, simplicity, or other functional form constraints. For…
In severe viral pneumonia, including Coronavirus disease 2019 (COVID-19), the viral replication phase is often followed by hyperinflammation (‘cytokine storm syndrome’), which can lead to acute respiratory distress syndrome, multi…
We develop a heterogeneous-agents network-based model to analyze alternative policies during a pandemic outbreak, accounting for health and economic trade-offs within the same empirical framework. We leverage a variety of data…
How costly is foreclosure? Estimates of the social cost of foreclosure typically focus on financial costs. Using random judge assignment instrumental variable (IV) and propensity score matching (PSM) approaches in Cook County…
Consumer data is increasingly available to firms through private exchanges. We study a voluntary monitoring program by a major U.S. auto insurer, in which drivers accept short-term tracking in exchange for potential discounts on…
We study how opening to trade affects economic growth in a model where heterogeneous firms can adopt new technologies already in use by other firms in their home country. We characterize the growth rate using a summary statistic…
We consider a variant of the contextual bandit problem. In standard contextual bandits, when a user arrives we get the user’s complete feature vector and then assign a treatment (arm) to that user. In a number of applications…
We model a simple market setting in which fragmentation of trade of the same asset across multiple exchanges improves allocative efficiency. Fragmentation reduces the inhibiting effect of price-impact avoidance on order submission…
Adaptive experiment designs can dramatically improve statistical efficiency in randomized trials, but they also complicate statistical inference. For example, it is now well known that the sample mean is biased in adaptive trials…
The allocation of decision authority by a principal to either a human agent or an artificial intelligence (AI) is examined. The principal trades off an AI’s more aligned choice with the need to motivate the human agent to expend…
For many machine learning algorithms, two main assumptions are required to guarantee performance. One is that the test data are drawn from the same distribution as the training data, and the other is that the model is correctly…