Publications
The Causal Effect of Parent Occupation on Child Occupation: A Multivalued Treatment with Positivity Constraints. Ian Lundberg, Daniel Molitor, Jennie Brand (2025). Sociological Methods and Research.Abstract
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 categorical values. A central methodological hurdle is positivity: some occupations (e.g., lawyer) are simply never held by some parents (e.g., those who did not complete college). We show how to overcome this hurdle by reporting summaries within subgroups that focus attention on the causal quantities that can be credibly estimated. Future research should build on the longstanding tradition of descriptive mobility research to answer causal questions.
Abstract
The COVID-19 public health emergency caused widespread economic shutdown and unemployment. The resulting surge in Unemployment Insurance claims threatened to overwhelm the legacy systems state workforce agencies rely on to collect, process, and pay claims. In Rhode Island, we developed a scalable cloud solution to collect Pandemic Unemployment Assistance claims as part of a new program created under the Coronavirus Aid, Relief and Economic Security Act to extend unemployment benefits to independent contractors and gig-economy workers not covered by traditional Unemployment Insurance. Our new system was developed, tested, and deployed within 10 days following the passage of the Coronavirus Aid, Relief and Economic Security Act, making Rhode Island the first state in the nation to collect, validate, and pay Pandemic Unemployment Assistance claims. A cloud-enhanced interactive voice response system was deployed a week later to handle the corresponding surge in weekly certifications for continuing unemployment benefits. Cloud solutions can augment legacy systems by offloading processes that are more efficiently handled in modern scalable systems, reserving the limited resources of legacy systems for what they were originally designed. This agile use of combined technologies allowed Rhode Island to deliver timely Pandemic Unemployment Assistance benefits with an estimated cost savings of $502,000 (representing a 411% return on investment).
Pre-prints
Adaptive Randomization in Conjoint Survey Experiments. Jennah Gosciak, Daniel Molitor, Ian Lundberg. SocArXiv.Abstract
Human choices are often multi-dimensional. For example, a person deciding which of two immigrants is more worthy of admission to a country might weigh the prospective immigrants’ education, age, country of origin, and employment history. Conjoint experiments have rapidly generated new insight into these multidimensional choices. By independently randomizing the attributes of a pair of fictitious profiles, researchers summarize the average contribution that each attribute makes to an overall choice. But what if the effect of one attribute depends on the values of other attributes? We present a method that uses data-adaptive experimentation to search for heterogeneity in the effect of one focal attribute as a function of all other attributes. Our empirical application of this method shows that U.S. adults weigh the education of an immigrant much more heavily for certain immigrants than for others. By targeting the heterogeneous effects of a focal attribute, our approach complements conjoint designs that target the average effects of all attributes.
Abstract
The mismatch between the skills that employers seek and the skills that workers possess will increase substantially as demand for technically skilled workers accelerates. Skill mismatches disproportionately affect low-income workers and those within industries where relative demand growth for technical skills is strongest. As a result, much emphasis is placed on reskilling workers to ease transitions into new careers. However, utilization of training programs may be sub-optimal if workers are uncertain about the returns to their investment in training. While the U.S. spends billions of dollars annually on reskilling programs and unemployment insurance, there are few measures of program effectiveness that workers or government can use to guide training investment and ensure valuable reskilling outcomes. We demonstrate a causal machine learning method for estimating the value-added returns to training programs in Rhode Island, where enrollment increases future quarterly earnings by $605 on average, ranging from -$1,570 to $3,470 for individual programs. In a nationwide survey (N=2,014), workers prefer information on the value-added returns to earnings following training enrollment, establishing the importance of our estimates for guiding training decisions. For every 10% increase in expected earnings, workers are 17.4% more likely to express interest in training. State and local governments can provide this preferred information on value-added returns using our method and existing administrative data.
Works in Progress
Anytime-Valid Inference in Conjoint Experiments. Daniel Molitor and Jennah Gosciak.Abstract
Conjoint experiments have become increasingly popular for studying how multiple attributes influence decision-making. However, determining the optimal sample size required to achieve adequate statistical power in conjoint experiments is challenging; conventional power analysis requires many assumptions to hold simultaneously, and can easily under- or over-estimate the necessary sample size. To overcome these limitations, I propose an alternative approach grounded in recent advances in anytime-valid inference. Rather than relying on conventional power analysis, this approach introduces anytime-valid confidence sequences (CSs) and corresponding p-values for key conjoint estimands, including the AMCE, ACIE, and marginal means. These procedures are computationally simple—building on standard regression outputs, guarantee valid Type I error control at any stopping point, and enable practitioners to continuously monitor their empirical estimates and implement data-driven stopping rules once their estimates of interest achieve sufficient statistical power or precision. In simulations calibrated to real-world conjoint studies, I show that this approach preserves nominal coverage, achieves comparable power to standard fixed-n approaches, and yields average sample savings of 10–40% across a broad range of effect sizes, sample sizes, and attribute levels. This approach gives practitioners a principled, efficient way to determine when to stop data collection without relying on pre-specified power analyses.
Abstract
We develop methods for anytime-valid causal inference on the Average Treatment Effect (ATE) in adaptive experiments, addressing two challenges of particular relevance to applied researchers: high variance (low precision) in ATE estimates, and imbalanced statistical power across treatment arms. We propose MADCovar, which substantially improves ATE precision through covariate adjustment, and MADMod, which adaptively reallocates sample to underpowered treatments to improve power across all arms. These methods allow researchers to run adaptive experiments that are both statistically rigorous and practically useful—offering greater control over inference while preserving the efficiency benefits of adaptive designs.