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.
You may search for authors and topics and download copies of the work there.
Switchback Price Experiments with Forward-Looking Demand
We consider a retailer running a switchback experiment for the price of a single product, with infinite supply. In each period, the seller chooses a price from a set of predefined prices that consist of a reference price and a few discounted…
When Does Interference Matter? Decision-Making in Platform Experiments
This paper investigates decision-making in A/B experiments for online platforms and marketplaces. In such settings, due to constraints on inventory, A/B experiments typically lead to biased estimators because of interference; this phenomenon has…
Designing Algorithmic Recommendations to Achieve Human–AI Complementarity
Algorithms frequently assist, rather than replace, human decision-makers. However, the design and analysis of algorithms often focus on predicting outcomes and do not explicitly model their effect on human decisions. This discrepancy between the…
Transitional Market Dynamics in Complex Environments
This paper presents a new approach to modeling transitional dynamics in dynamic models of imperfect competition, a crucial yet often neglected aspect of empirical models in industrial organization that seek to understand market responses to…
Price Experimentation and Interference
In this paper, we examine biases arising in A/B tests where firms modify a continuous parameter, such as price, to estimate the global treatment effect of a given performance metric, such as profit. These biases emerge in canonical experimental…
Do Mergers and Acquisitions Improve Efficiency? Evidence from Power Plants
Using rich data on hourly physical productivity and thousands of ownership changes from U.S. power plants, we study the effects of acquisitions on efficiency and underlying mechanisms. We find a 2% average increase in efficiency for acquired…
Evaluating Treatment Prioritization Rules via Rank-Weighted Average Treatment Effects
There are a number of available methods for selecting whom to prioritize for treatment, including ones based on treatment effect estimation, risk scoring, and handcrafted rules. We propose rank-weighted average treatment effect (RATE) metrics as…
Generative AI at Work
New AI tools have the potential to change the way workers perform and learn, but little is known about their impacts on the job. In this paper, we study the staggered introduction of a generative AI-based conversational assistant using data from…
Area Conditions and Positive Incentives: Engaging Local Communities to Protect Forests
Tropical deforestation for agriculture causes alarming CO2 emissions and loss of biodiversity and ecosystem services. To prevent this, various governments and multinational commodity-buyers offer a positive incentive for locals conditional on no…
The Digital Welfare of Nations: New Measures of Welfare Gains and Inequality
Digital goods can generate large benefits for consumers, but these benefits are largely unmeasured in the national accounts, including GDP and productivity. In this paper, we measure welfare gains from 10 popular digital goods across 13 countries…
Market Re-Design of Framework Agreements in Chile Reduces Government Procurement Spending
Framework agreements (FAs) are procurement mechanisms used in private and public organizations by which a central procurement agency selects an assortment of products, typically through auctions, and then affiliated organizations can purchase…
On Frequentist Regret of Linear Thompson Sampling
This paper studies the stochastic linear bandit problem, where a decision-maker chooses actions from possibly time-dependent sets of vectors in ℝd and receives noisy rewards. The objective is to minimize regret, the difference between the…
Economics of Grid-Scale Energy Storage in Wholesale Electricity Markets
I investigate the incentives for investing and operating grid-scale energy storage in electricity markets and the need for policies to complement investments with renewables. I develop a new dynamic equilibrium framework that allows for storage’s…
Neural Design for Genetic Perturbation Experiments
The problem of how to genetically modify cells in order to maximize a certain cellular phenotype has taken center stage in drug development over the last few years (with, for example, genetically edited CAR-T, CAR-NK, and CAR-NKT cells entering…
Boosting Sales and Customer Welfare from Premade Foods (Let the Freshest Chicken Fly Off the Shelf First)
[Submitted to Management Science.]
This paper examines a grocery retailer’s management of a premade food product. The retailer’s goal is to maximize a weighted sum of direct profit and customer welfare. Multiple items of the…
Data Tracking under Competition
We explore the welfare implications of data-tracking technologies that enable firms to collect consumer data and use it for price discrimination. The model we develop centers around two features: competition between firms and consumers’ level of…
Speed Up the Cold-Start Learning in Two-Sided Bandits with Many Arms
Multi-armed bandit (MAB) algorithms are efficient approaches to reduce the opportunity cost of online experimentation and are used by companies to find the best product from periodically refreshed product catalogs. However, these algorithms face…
Advertising Media and Target Audience Optimization via High-dimensional Bandits
We present a data-driven algorithm that advertisers can use to automate their digital ad-campaigns at online publishers. The algorithm enables the advertiser to search across available target audiences and ad-media to find the best possible…
Predicting Cellular Responses with Variational Causal Inference and Refined Relational Information
Predicting the responses of a cell under perturbations may bring important benefits to drug discovery and personalized therapeutics. In this work, we propose a novel graph variational Bayesian causal inference framework to predict a cell’s gene…
SystemMatch: Optimizing Preclinical Drug Models to Human Clinical Outcomes via Generative Latent-Space Matching
Translating the relevance of preclinical models (in vitro, animal models, or organoids) to their relevance in humans presents an important challenge during drug development. The rising abundance of single-cell genomic data from human tumors and…