Ian Lundberg
Biographic Data
| ID | 248533 |
|---|---|
| NAME | Ian Lundberg |
| GIVEN NAMES | Ian |
| FAMILY NAME | Lundberg |
| SIGNATURE | LUNDBERG I |
| AFFILIATIONS | Princeton University |
| ORCID | 0000-0002-1909-2270 |
| VERIFIED | Yes |
| TOTAL WORKS | 14 |
| TOTAL CITATIONS | 217 |
| AUTHOR COUNT | 14 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2017 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 6 |
Causal Inference with a Continuous Treatment: Addressing Positivity Constraints, Nonlinearity, and Effect Heterogeneity
Causal inference approaches often emphasize binary treatments. But in many applications, the underlying constructs are continuous. In the potential outcomes framework, a continuous treatment can take on numerous values, each corresponding to a potential outcome that may be realized. In this setting, common estimands may be intractable because of a common issue in social research, particularly research on social inequality: the exposure is highly …
Adaptive Randomization in Conjoint Survey Experiments
Human choices are often both multi-dimensional and interactive. For example, a person deciding which of two immigrants is more worthy of admission to a country might weigh their education, and the weight placed on education may depend on other factors, such as their age, country of origin and employment history. We develop a response-adaptive experimental design that summarizes the range of effects of one attribute as a function of all other attr…
The Causal Impact of Segregation on a Disparity: A Gap-Closing Approach
Segregation-whether across schools, neighborhoods, or occupations-is regularly invoked as a cause of social and economic disparities. But segregation is a complicated causal treatment: what do we mean when we appeal to a world in which segregation does not exist? One could take societal contexts as the unit of analysis and compare across societies with differing levels of segregation. In practice, it is more common for studies of segregation to t…
The Causal Effect of Parent Occupation on Child Occupation: A Multivalued Treatment with Positivity Constraints
To what degree does parent occupation cause a child's occupational attainment? We articulate this causal question in the potential outcomes framework. Empirically, we show that adjustment for only two confounding variables substantially reduces the estimated association between parent and child occupation in a U.S. cohort. Methodologically, we highlight complications that arise when the treatment variable (parent occupation) can take many categor…
The Gap-Closing Estimand: A Causal Approach to Study Interventions That Close Disparities Across Social Categories
Disparities across race, gender, and class are important targets of descriptive research. But rather than only describe disparities, research would ideally inform interventions to close those gaps. The gap-closing estimand quantifies how much a gap (e.g., incomes by race) would close if we intervened to equalize a treatment (e.g., access to college). Drawing on causal decomposition analyses, this type of research question yields several benefits.…
Researcher reasoning meets computational capacity: Machine learning for social science
What Is Your Estimand? Defining the Target Quantity Connects Statistical Evidence to Theory
We make only one point in this article. Every quantitative study must be able to answer the question: what is your estimand? The estimand is the target quantity-the purpose of the statistical analysis. Much attention is already placed on how to do estimation; a similar degree of care should be given to defining the thing we are estimating. We advocate that authors state the central quantity of each analysis-the theoretical estimand-in precise ter…
Measuring the predictability of life outcomes with a scientific mass collaboration
How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the common task method; 160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly…
Government Assistance Protects Low‐Income Families from Eviction
A lack of affordable housing is a pressing issue for many low-income American families and can lead to eviction from their homes. Housing assistance programs to address this problem include public housing and other assistance, including vouchers, through which a government agency offsets the cost of private market housing. This paper assesses whether the receipt of either category of assistance reduces the probability that a family will be evicte…
Does Opportunity Skip Generations? Reassessing Evidence From Sibling and Cousin Correlations
Sibling (cousin) correlations are empirically straightforward: they capture the degree to which siblings’ (cousins’) socioeconomic outcomes are similar. At face value, these quantities seem to summarize something about how families constrain opportunity. Their meaning, however, is complicated. One empirical set of sibling and cousin correlations can be generated from a multitude of distinct theoretical processes. I illustrate this problem in the …
A Research Note on the Prevalence of Housing Eviction Among Children Born in U.S. Cities
A growing body of research suggests that housing eviction is more common than previously recognized and may play an important role in the reproduction of poverty. The proportion of children affected by housing eviction, however, remains largely unknown. We estimate that one in seven children born in large U.S. cities in 1998–2000 experienced at least one eviction for nonpayment of rent or mortgage between birth and age 15. Rates of eviction were …
Introduction to the Special Collection on the Fragile Families Challenge
The Fragile Families Challenge is a scientific mass collaboration designed to measure and understand the predictability of life trajectories. Participants in the Challenge created predictive models of six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. This Special Collection includes 12 articles describing participants' approaches to predicting these six outcomes as well as 3 artic…
Privacy, Ethics, and Data Access: A Case Study of the Fragile Families Challenge
Stewards of social data face a fundamental tension. On one hand, they want to make their data accessible to as many researchers as possible to facilitate new discoveries. At the same time, they want to restrict access to their data as much as possible to protect the people represented in the data. In this article, we provide a case study addressing this common tension in an uncommon setting: the Fragile Families Challenge, a scientific mass colla…
New Evidence Against a Causal Marriage Wage Premium
Recent research has shown that men’s wages rise more rapidly than expected prior to marriage, but interpretations diverge on whether this indicates selection or a causal effect of anticipating marriage. We seek to adjudicate this debate by bringing together literatures on (1) the male marriage wage premium; (2) selection into marriage based on men’s economic circumstances; and (3) the transition to adulthood, during which both union formation and…
What Is Your Estimand? Defining the Target Quantity Connects Statistical Evidence to Theory
We make only one point in this article. Every quantitative study must be able to answer the question: what is your estimand? The estimand is the target quantity-the purpose of the statistical analysis. Much attention is already placed on how to do estimation; a similar degree of care should be given to defining the thing we are estimating. We advocate that authors state the central quantity of each analysis-the theoretical estimand-in precise ter…
New Evidence Against a Causal Marriage Wage Premium
Recent research has shown that men’s wages rise more rapidly than expected prior to marriage, but interpretations diverge on whether this indicates selection or a causal effect of anticipating marriage. We seek to adjudicate this debate by bringing together literatures on (1) the male marriage wage premium; (2) selection into marriage based on men’s economic circumstances; and (3) the transition to adulthood, during which both union formation and…
Introduction to the Special Collection on the Fragile Families Challenge
The Fragile Families Challenge is a scientific mass collaboration designed to measure and understand the predictability of life trajectories. Participants in the Challenge created predictive models of six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. This Special Collection includes 12 articles describing participants' approaches to predicting these six outcomes as well as 3 artic…
The Gap-Closing Estimand: A Causal Approach to Study Interventions That Close Disparities Across Social Categories
Disparities across race, gender, and class are important targets of descriptive research. But rather than only describe disparities, research would ideally inform interventions to close those gaps. The gap-closing estimand quantifies how much a gap (e.g., incomes by race) would close if we intervened to equalize a treatment (e.g., access to college). Drawing on causal decomposition analyses, this type of research question yields several benefits.…
A Research Note on the Prevalence of Housing Eviction Among Children Born in U.S. Cities
A growing body of research suggests that housing eviction is more common than previously recognized and may play an important role in the reproduction of poverty. The proportion of children affected by housing eviction, however, remains largely unknown. We estimate that one in seven children born in large U.S. cities in 1998–2000 experienced at least one eviction for nonpayment of rent or mortgage between birth and age 15. Rates of eviction were …
Does Opportunity Skip Generations? Reassessing Evidence From Sibling and Cousin Correlations
Sibling (cousin) correlations are empirically straightforward: they capture the degree to which siblings’ (cousins’) socioeconomic outcomes are similar. At face value, these quantities seem to summarize something about how families constrain opportunity. Their meaning, however, is complicated. One empirical set of sibling and cousin correlations can be generated from a multitude of distinct theoretical processes. I illustrate this problem in the …
Government Assistance Protects Low‐Income Families from Eviction
A lack of affordable housing is a pressing issue for many low-income American families and can lead to eviction from their homes. Housing assistance programs to address this problem include public housing and other assistance, including vouchers, through which a government agency offsets the cost of private market housing. This paper assesses whether the receipt of either category of assistance reduces the probability that a family will be evicte…
Privacy, Ethics, and Data Access: A Case Study of the Fragile Families Challenge
Stewards of social data face a fundamental tension. On one hand, they want to make their data accessible to as many researchers as possible to facilitate new discoveries. At the same time, they want to restrict access to their data as much as possible to protect the people represented in the data. In this article, we provide a case study addressing this common tension in an uncommon setting: the Fragile Families Challenge, a scientific mass colla…
Researcher reasoning meets computational capacity: Machine learning for social science
New Evidence Against a Causal Marriage Wage Premium
Recent research has shown that men’s wages rise more rapidly than expected prior to marriage, but interpretations diverge on whether this indicates selection or a causal effect of anticipating marriage. We seek to adjudicate this debate by bringing together literatures on (1) the male marriage wage premium; (2) selection into marriage based on men’s economic circumstances; and (3) the transition to adulthood, during which both union formation and…
A Research Note on the Prevalence of Housing Eviction Among Children Born in U.S. Cities
A growing body of research suggests that housing eviction is more common than previously recognized and may play an important role in the reproduction of poverty. The proportion of children affected by housing eviction, however, remains largely unknown. We estimate that one in seven children born in large U.S. cities in 1998–2000 experienced at least one eviction for nonpayment of rent or mortgage between birth and age 15. Rates of eviction were …
Introduction to the Special Collection on the Fragile Families Challenge
The Fragile Families Challenge is a scientific mass collaboration designed to measure and understand the predictability of life trajectories. Participants in the Challenge created predictive models of six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. This Special Collection includes 12 articles describing participants' approaches to predicting these six outcomes as well as 3 artic…
Privacy, Ethics, and Data Access: A Case Study of the Fragile Families Challenge
Stewards of social data face a fundamental tension. On one hand, they want to make their data accessible to as many researchers as possible to facilitate new discoveries. At the same time, they want to restrict access to their data as much as possible to protect the people represented in the data. In this article, we provide a case study addressing this common tension in an uncommon setting: the Fragile Families Challenge, a scientific mass colla…
Measuring the predictability of life outcomes with a scientific mass collaboration
How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the common task method; 160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly…
Government Assistance Protects Low‐Income Families from Eviction
A lack of affordable housing is a pressing issue for many low-income American families and can lead to eviction from their homes. Housing assistance programs to address this problem include public housing and other assistance, including vouchers, through which a government agency offsets the cost of private market housing. This paper assesses whether the receipt of either category of assistance reduces the probability that a family will be evicte…
Does Opportunity Skip Generations? Reassessing Evidence From Sibling and Cousin Correlations
Sibling (cousin) correlations are empirically straightforward: they capture the degree to which siblings’ (cousins’) socioeconomic outcomes are similar. At face value, these quantities seem to summarize something about how families constrain opportunity. Their meaning, however, is complicated. One empirical set of sibling and cousin correlations can be generated from a multitude of distinct theoretical processes. I illustrate this problem in the …
What Is Your Estimand? Defining the Target Quantity Connects Statistical Evidence to Theory
We make only one point in this article. Every quantitative study must be able to answer the question: what is your estimand? The estimand is the target quantity-the purpose of the statistical analysis. Much attention is already placed on how to do estimation; a similar degree of care should be given to defining the thing we are estimating. We advocate that authors state the central quantity of each analysis-the theoretical estimand-in precise ter…
Researcher reasoning meets computational capacity: Machine learning for social science
The Gap-Closing Estimand: A Causal Approach to Study Interventions That Close Disparities Across Social Categories
Disparities across race, gender, and class are important targets of descriptive research. But rather than only describe disparities, research would ideally inform interventions to close those gaps. The gap-closing estimand quantifies how much a gap (e.g., incomes by race) would close if we intervened to equalize a treatment (e.g., access to college). Drawing on causal decomposition analyses, this type of research question yields several benefits.…
Adaptive Randomization in Conjoint Survey Experiments
Human choices are often both multi-dimensional and interactive. For example, a person deciding which of two immigrants is more worthy of admission to a country might weigh their education, and the weight placed on education may depend on other factors, such as their age, country of origin and employment history. We develop a response-adaptive experimental design that summarizes the range of effects of one attribute as a function of all other attr…
The Causal Impact of Segregation on a Disparity: A Gap-Closing Approach
Segregation-whether across schools, neighborhoods, or occupations-is regularly invoked as a cause of social and economic disparities. But segregation is a complicated causal treatment: what do we mean when we appeal to a world in which segregation does not exist? One could take societal contexts as the unit of analysis and compare across societies with differing levels of segregation. In practice, it is more common for studies of segregation to t…
The Causal Effect of Parent Occupation on Child Occupation: A Multivalued Treatment with Positivity Constraints
To what degree does parent occupation cause a child's occupational attainment? We articulate this causal question in the potential outcomes framework. Empirically, we show that adjustment for only two confounding variables substantially reduces the estimated association between parent and child occupation in a U.S. cohort. Methodologically, we highlight complications that arise when the treatment variable (parent occupation) can take many categor…
Causal Inference with a Continuous Treatment: Addressing Positivity Constraints, Nonlinearity, and Effect Heterogeneity
Causal inference approaches often emphasize binary treatments. But in many applications, the underlying constructs are continuous. In the potential outcomes framework, a continuous treatment can take on numerous values, each corresponding to a potential outcome that may be realized. In this setting, common estimands may be intractable because of a common issue in social research, particularly research on social inequality: the exposure is highly …
Mathematics (7 works) · Sociology (5 works) · Statistics (5 works) · Advanced Causal Inference Techniques (4 works) · Computer Science (4 works) · Psychology (4 works) · Demographic economics (3 works) · Developmental psychology (3 works) · Econometrics (3 works) · Economics (3 works)