About

Hiroaki Kaido is an Associate Professor in the Department of Economics at Boston University. He holds a Ph.D. in Economics from the University of California, San Diego, earned in 2010. Prior to that, he obtained an M.A. in Economics from Hitotsubashi University in 2005 and a B.A. in Economics from Osaka University in 2003. His academic and research interests focus on econometric theory, microeconometrics, and empirical finance. Through his work, Professor Kaido contributes to advancing the understanding and application of econometric methods in analyzing economic and financial data.

Research topics

• Computer Science
• Data Mining
• Econometrics
• Artificial Intelligence
• Mathematics
• Mathematical optimization
• Geometry
• Statistics
• Mathematical economics

Selected publications

• Universal Inference for Incomplete Models

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Universal Inference for Incomplete Discrete Choice Models

  ArXiv.org · 2025-01-29

  preprintOpen access1st authorCorresponding

  A growing number of empirical models exhibit set-valued predictions. This paper develops a tractable inference method with finite-sample validity for such models. The proposed procedure uses a robust version of the universal inference framework by Wasserman et al. (2020) and avoids using moment selection tuning parameters, resampling, or simulations. The method is designed for constructing confidence intervals for counterfactual objects and other functionals of the underlying parameter. It can be used in applications that involve model incompleteness, discrete and continuous covariates, and parameters containing nuisance components.

  PublisherOA PDFDOI

• Testing Exclusion and Shape Restrictions in Potential Outcomes Models

  arXiv (Cornell University) · 2025-12-24

  preprintOpen access1st authorCorresponding

  Exclusion and shape restrictions play a central role in defining causal effects and interpreting estimates in potential outcomes models. To date, the testable implications of such restrictions have been studied on a case-by-case basis in a limited set of models. In this paper, we develop a general framework for characterizing sharp testable implications of general support restrictions on the potential response functions, based on a novel graph-based representation of the model. The framework provides a unified and constructive method for deriving all observable implications of the modeling assumptions. We illustrate the approach in several popular settings, including instrumental variables, treatment selection, mediation, and interference. As an empirical application, we revisit the US Lung Health Study and test for the presence of spillovers between spouses, specification of exposure maps, and persistence of treatment effects over time.

  PublisherDOI

• Testing Exclusion and Shape Restrictions in Potential Outcomes Models

  ArXiv.org · 2025-12-24

  articleOpen access1st authorCorresponding

  Exclusion and shape restrictions play a central role in defining causal effects and interpreting estimates in potential outcomes models. To date, the testable implications of such restrictions have been studied on a case-by-case basis in a limited set of models. In this paper, we develop a general framework for characterizing sharp testable implications of general support restrictions on the potential response functions, based on a novel graph-based representation of the model. The framework provides a unified and constructive method for deriving all observable implications of the modeling assumptions. We illustrate the approach in several popular settings, including instrumental variables, treatment selection, mediation, and interference. As an empirical application, we revisit the US Lung Health Study and test for the presence of spillovers between spouses, specification of exposure maps, and persistence of treatment effects over time.

  PublisherOA PDF

• Information Based Inference in Models with Set-Valued Predictions and Misspecification

  arXiv (Cornell University) · 2024-01-19

  preprintOpen access1st authorCorresponding

  This paper proposes an information-based inference method for partially identified parameters in incomplete models that is valid both when the model is correctly specified and when it is misspecified. Key features of the method are: (i) it is based on minimizing a suitably defined Kullback-Leibler information criterion that accounts for incompleteness of the model and delivers a non-empty pseudo-true set; (ii) it is computationally tractable; (iii) its implementation is the same for both correctly and incorrectly specified models; (iv) it exploits all information provided by variation in discrete and continuous covariates; (v) it relies on Rao's score statistic, which is shown to be asymptotically pivotal.

  PublisherOA PDFDOI

• Information based inference in models with set-valued predictions and misspecification

  2024-01-29 · 1 citations

  reportOpen access1st authorCorresponding

  This paper proposes an information-based inference method for partially identified parameters in incomplete models that is valid both when the model is correctly specified and when it is misspecified.Key features of the method are: (i) it is based on minimizing a suitably defined Kullback-Leibler information criterion that accounts for incompleteness of the model and delivers a non-empty pseudotrue set; (ii) it is computationally tractable; (iii) its implementation is the same for both correctly and incorrectly specified models; (iv) it exploits all information provided by variation in discrete and continuous covariates; (v) it relies on Rao's score statistic, which is shown to be asymptotically pivotal.

  PublisherOA PDFDOI

• Set-Valued Control Functions

  ArXiv.org · 2024-03-01

  preprintOpen accessSenior author

  The control function approach allows the researcher to identify various causal effects of interest. While powerful, it requires a strong invertibility assumption in the selection process, which limits its applicability. This paper expands the scope of the nonparametric control function approach by allowing the control function to be set-valued and derive sharp bounds on structural parameters. The proposed generalization accommodates a wide range of selection processes involving discrete endogenous variables, random coefficients, treatment selections with interference, and dynamic treatment selections. The framework also applies to partially observed or identified controls that are directly motivated from economic models.

  PublisherOA PDFDOI

• Nonparametric identification of random coefficients in aggregate demand models for differentiated products

  Econometrics Journal · 2023-01-03 · 3 citations

  articleOpen accessSenior authorCorresponding

  Summary This paper studies nonparametric identification in market-level demand models for differentiated products with heterogeneous consumers. We consider a general class of models that allows for the individual-specific coefficients to vary continuously across the population and give conditions under which the density of these coefficients, and hence also functionals such as the fractions of individuals who benefit from a counterfactual intervention, is identified.

  PublisherOA PDFDOI

• Applications of Choquet expected utility to hypothesis testing with incompleteness

  Japanese Economic Review · 2023-10-01 · 1 citations

  articleOpen access1st authorCorresponding

  Abstract The Maximin and Choquet expected utility theories guide decision-making under ambiguity. We apply them to hypothesis testing in incomplete models. We consider a statistical risk function that uses a prior probability to incorporate parameter uncertainty and a belief function to reflect the decision-maker’s willingness to be robust against the model’s incompleteness. We develop a numerical method to implement a test that minimizes the risk function. We also use a sequence of such tests to approximate a minimax optimal test when a nuisance parameter is present under the null hypothesis.

  PublisherOA PDFDOI

• Robust Tests of Model Incompleteness in the Presence of Nuisance Parameters

  arXiv (Cornell University) · 2022-08-24 · 3 citations

  preprintOpen accessSenior author

  Economic models may exhibit incompleteness depending on whether or not they admit certain policy-relevant features such as strategic interaction, self-selection, or state dependence. We develop a novel test of model incompleteness and analyze its asymptotic properties. A key observation is that one can identify the least-favorable parametric model that represents the most challenging scenario for detecting local alternatives without knowledge of the selection mechanism. We build a robust test of incompleteness on a score function constructed from such a model. The proposed procedure remains computationally tractable even with nuisance parameters because it suffices to estimate them only under the null hypothesis of model completeness. We illustrate the test by applying it to a market entry model and a triangular model with a set-valued control function.

  PublisherOA PDFDOI

Recent grants

• Collaborative Research: Robust Inference and Computational Methods for Optimal Values of Nonlinear Programs

  NSF · $120k · 2018–2020

• Robust Inference and Specification Analysis in Incomplete Models

  NSF · $272k · 2020–2024

Frequent coauthors

• Kaspar Wüthrich

  53 shared

• Shuowen Chen

  50 shared

• Enrico De Giorgi

  University of California, Berkeley

  49 shared

• Bryan S. Graham

  University of California, Berkeley

  49 shared

• Andrin Pelican

  49 shared

• Bo E. Honoré

  49 shared

• Xun Tang

  49 shared

• Gabriel Okasa

  Swiss National Science Foundation

  49 shared

Education

• Ph.D.

  University of California, San Diego