Publications

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Article

Estimation of the Number of Components in Nonparametric Multivariate Finite Mixture Models

By: Caleb Kwon and Eric Mbakop

November 2021
Article

Panel Experiments and Dynamic Causal Effects: A Finite Population Perspective

By: Iavor Bojinov, Ashesh Rambachan and Neil Shephard

In panel experiments, we randomly assign units to different interventions, measuring their outcomes, and repeating the procedure in several periods. Using the potential outcomes framework, we define finite population dynamic causal effects that capture the relative... View Details

Keywords: Panel Data; Dynamic Causal Effects; Potential Outcomes; Finite Population; Nonparametric; Mathematical Methods

2019
Article

Time Series Experiments and Causal Estimands: Exact Randomization Tests and Trading

By: Iavor I Bojinov and Neil Shephard

We define causal estimands for experiments on single time series, extending the potential outcome framework to dealing with temporal data. Our approach allows the estimation of a broad class of these estimands and exact randomization based p-values for testing causal... View Details

Keywords: Causality; Nonparametric; Potential Outcomes; Trading Costs; Mathematical Methods

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Article

Fast Generalized Subset Scan for Anomalous Pattern Detection

By: Edward McFowland III, Skyler Speakman and Daniel B. Neill

We propose Fast Generalized Subset Scan (FGSS), a new method for detecting anomalous patterns in general categorical data sets. We frame the pattern detection problem as a search over subsets of data records and attributes, maximizing a nonparametric scan statistic... View Details

Keywords: Pattern Detection; Anomaly Detection; Knowledge Discovery; Bayesian Networks; Scan Statistics; Analytics and Data Science

August 2006
Article

Confidence Intervals for Probabilities of Default

By: Samuel G. Hanson and Til Schuermann

In this paper we conduct a systematic comparison of confidence intervals around estimated probabilities of default (PD) using several analytical approaches as well as parametric and nonparametric bootstrap methods. We do so for two different PD estimation... View Details

Keywords: Credit Risk; Bootstrap; Mathematical Methods; Credit; Risk Management

2024
Working Paper

What Drives Variation in Investor Portfolios? Estimating the Roles of Beliefs and Risk Preferences

By: Mark Egan, Alexander MacKay and Hanbin Yang

We present an empirical model of portfolio choice that allows for the nonparametric estimation of investors' (subjective) expectations and risk preferences. Utilizing a comprehensive dataset of 401(k) plans from 2009 through 2019, we explore heterogeneity in asset... View Details

Keywords: Stock Market Expectations; Demand Estimation; Retirement Planning; Defined Contribution Retirement Plan; 401 (K); Finance; Investment Portfolio; Investment; Retirement; Behavioral Finance; Financial Services Industry; United States

SSRN

2020
Working Paper

A General Theory of Identification

By: Iavor Bojinov and Guillaume Basse

What does it mean to say that a quantity is identifiable from the data? Statisticians seem to agree on a definition in the context of parametric statistical models — roughly, a parameter θ in a model P = {Pθ : θ ∈ Θ} is identifiable if the mapping θ 7→ Pθ is injective.... View Details

Keywords: Identification; Econometric Models; Analytics and Data Science; Theory

2023
Working Paper

Efficient Discovery of Heterogeneous Quantile Treatment Effects in Randomized Experiments via Anomalous Pattern Detection

By: Edward McFowland III, Sriram Somanchi and Daniel B. Neill

In the recent literature on estimating heterogeneous treatment effects, each proposed method makes its own set of restrictive assumptions about the intervention’s effects and which subpopulations to explicitly estimate. Moreover, the majority of the literature provides... View Details

Keywords: Causal Inference; Program Evaluation; Algorithms; Distributional Average Treatment Effect; Treatment Effect Subset Scan; Heterogeneous Treatment Effects

2024
Working Paper

Anytime-Valid Inference in Linear Models and Regression-Adjusted Causal Inference

By: Michael Lindon, Dae Woong Ham, Martin Tingley and Iavor I. Bojinov

Linear regression adjustment is commonly used to analyze randomized controlled experiments due to its efficiency and robustness against model misspecification. Current testing and interval estimation procedures leverage the asymptotic distribution of such estimators to... View Details

Keywords: Mathematical Methods; Analytics and Data Science

2022
Working Paper

Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments

By: Kosuke Imai and Michael Lingzhi Li

Researchers are increasingly turning to machine learning (ML) algorithms to investigate causal heterogeneity in randomized experiments. Despite their promise, ML algorithms may fail to accurately ascertain heterogeneous treatment effects under practical settings with... View Details

Keywords: AI and Machine Learning; Mathematical Methods

April 2024
Article

Detecting Routines: Applications to Ridesharing CRM

By: Ryan Dew, Eva Ascarza, Oded Netzer and Nachum Sicherman

Routines shape many aspects of day-to-day consumption. While prior work has established the importance of habits in consumer behavior, little work has been done to understand the implications of routines—which we define as repeated behaviors with recurring, temporal... View Details

Keywords: Ride-sharing; Routine; Machine Learning; Customer Relationship Management; Consumer Behavior; Segmentation

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Forthcoming
Article

Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments

By: Kosuke Imai and Michael Lingzhi Li

Researchers are increasingly turning to machine learning (ML) algorithms to investigate causal heterogeneity in randomized experiments. Despite their promise, ML algorithms may fail to accurately ascertain heterogeneous treatment effects under practical settings with... View Details

Keywords: AI and Machine Learning; Mathematical Methods

Routines shape many aspects of day-to-day consumption. While prior work has established the importance of habits in consumer behavior, little work has been done to understand the implications of routines--which we define as repeated behaviors with recurring, temporal... View Details

Winter 2016
Article

Analytics for an Online Retailer: Demand Forecasting and Price Optimization

By: Kris J. Ferreira, Bin Hong Alex Lee and David Simchi-Levi

We present our work with an online retailer, Rue La La, as an example of how a retailer can use its wealth of data to optimize pricing decisions on a daily basis. Rue La La is in the online fashion sample sales industry, where they offer extremely limited-time... View Details

Keywords: Internet and the Web; Price; Forecasting and Prediction; Revenue; Sales; Retail Industry