More Like this

1. Regression-assisted Bayesian record linkage for causal inference in observational studies with covariates spread over two files

   https://doi.org/10.1016/j.jspi.2023.07.004  

   Guha, Sharmistha; Reiter, Jerome P (March 2024, Journal of Statistical Planning and Inference)

   We consider causal inference for observational studies with data spread over two files. One file includes the treatment, outcome, and some covariates measured on a set of individuals, and the other file includes additional causally-relevant covariates measured on a partially overlapping set of individuals. By linking records in the two databases, the analyst can control for more covariates, thereby reducing the risk of bias compared to using only one file alone. When analysts do not have access to a unique identifier that enables perfect, error-free linkages, they typically rely on probabilistic record linkage to construct a single linked data set, and estimate causal effects using these linked data. This typical practice does not propagate uncertainty from imperfect linkages to the causal inferences. Further, it does not take advantage of relationships among the variables to improve the linkage quality. We address these shortcomings by fusing regression-assisted, Bayesian probabilistic record linkage with causal inference. The Markov chain Monte Carlo sampler generates multiple plausible linked data files as byproducts that analysts can use for multiple imputation inferences. Here, we show results for two causal estimators based on propensity score overlap weights. Using simulations and data from the Italy Survey on Household Income and Wealth, we show that our approach can improve the accuracy of estimated treatment effects. 

   more » « less
2. Work In Progress: Considering the Impact on Research Quality of a Team Approach to Phenomenography

   https://doi.org/10.18260/1-2--33601  

   Fila, Nicholas D.; Zoltowski, Carla B.; Hess, Justin L.; Kim, Dayoung; Kerr, Alison J.; Brightman, Andrew O.; Loui, Michael (July 2019, Work In Progress: Considering the Impact on Research Quality of a Team Approach to Phenomenography)

   This WIP paper describes a team approach to phenomenography on ethical engineering practice in the health products industry and its potential impact on research quality. Although qualitative researchers often conduct phenomenography collaboratively, most often a single individual leads the data collection and analysis; others primarily serve as critical reviewers. However, quality may be enhanced by involving collaborators as data analysts in “sustained cycles of scrutiny, debate and testing against the data” [1, p. 88], thus interweaving unique perspectives and insights throughout the analysis process. Nonetheless, collaborating in this intensive data analysis process also presents unique challenges. In this paper, we (1) describe the processes we are applying in an integrated team-based phenomenographic study, (2) identify how the team approach affects research quality, and (3) reflect on the challenges inherent to this process. We ground this reflective case study in the methodological literature on phenomenography. Our team strategies include multiple interviewers (and, when possible, two interviewers per inter-view), team communication through reflective memos, and integration of individual and team-based data analysis with peer critique of individual analyses. We compare our team approach with typical individual phenomenographic approaches, and we align our procedures with the five strategies of the Qualifying Qualitative Research Quality Framework, or Q3, designed by Walther, Sochacka, and Kellam [2]. In aligning strategies, we consider benefits and trade-offs. 

   more » « less
3. Local Causal Discovery for Structural Evidence of Direct Discrimination

   https://doi.org/10.1609/aaai.v39i18.34130  

   Maasch, Jacqueline; Gan, Kyra; Chen, Violet; Orfanoudaki, Agni; Akpinar, Nil-Jana; Wang, Fei (April 2025, Proceedings of the AAAI Conference on Artificial Intelligence)

   Identifying the causal pathways of unfairness is a critical objective for improving policy design and algorithmic decision-making. Prior work in causal fairness analysis often requires knowledge of the causal graph, hindering practical applications in complex or low-knowledge domains. Moreover, global discovery methods that learn causal structure from data can display unstable performance on finite samples, preventing robust fairness conclusions. To mitigate these challenges, we introduce local discovery for direct discrimination (LD3): a method that uncovers structural evidence of direct unfairness by identifying the causal parents of an outcome variable. LD3 performs a linear number of conditional independence tests relative to variable set size, and allows for latent confounding under the sufficient condition that all parents of the outcome are observed. We show that LD3 returns a valid adjustment set (VAS) under a new graphical criterion for the weighted controlled direct effect, a qualitative indicator of direct discrimination. LD3 limits unnecessary adjustment, providing interpretable VAS for assessing unfairness. We use LD3 to analyze causal fairness in two complex decision systems: criminal recidivism prediction and liver transplant allocation. LD3 was more time-efficient and returned more plausible results on real-world data than baselines, which took 46× to 5870× longer to execute. 

   more » « less
4. CliniDial: A Naturally Occurring Multimodal Dialogue Dataset for Team Reflection in Action During Clinical Operation

   Deng, N; Das, K; Mihalcea, R; Popov, V; Abouelenien, M (June 2025, ACL 2025 Findings)

   In clinical operations, teamwork can be the crucial factor that determines the final outcome. Prior studies have shown that sufficient collaboration is the key factor that determines the outcome of an operation. To understand how the team practices teamwork during the operation, we collected CliniDial from simulations of medical operations. CliniDial includes the audio data and its transcriptions, the simulated physiology signals of the patient manikins, and how the team operates from two camera angles. We annotate behavior codes following an existing framework to understand the teamwork process for CliniDial. We pinpoint three main characteristics of our dataset, including its label imbalances, rich and natural interactions, and multiple modalities, and conduct experiments to test existing LLMs’ capabilities on handling data with these characteristics. Experimental results show that CliniDial poses significant challenges to the existing models, inviting future effort on developing methods that can deal with real-world clinical data. We open-source the codebase at https: //github.com/MichiganNLP/CliniDial.† 

   more » « less
5. One data set, many analysts: Implications for practicing scientists

   https://doi.org/10.3389/fpsyg.2023.1094150  

   Kummerfeld, Erich; Jones, Galin L (February 2023, Frontiers in Psychology)

   Researchers routinely face choices throughout the data analysis process. It is often opaque to readers how these choices are made, how they affect the findings, and whether or not data analysis results are unduly influenced by subjective decisions. This concern is spurring numerous investigations into the variability of data analysis results. The findings demonstrate that different teams analyzing the same data may reach different conclusions. This is the “many-analysts” problem. Previous research on the many-analysts problem focused on demonstrating its existence, without identifying specific practices for solving it. We address this gap by identifying three pitfalls that have contributed to the variability observed in many-analysts publications and providing suggestions on how to avoid them. 

   more » « less