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This paper addresses the methodology for achieving the user interface design reusability of a qualitative software system and effort minimization by applying the inference on the stored design documents. The pictorial design documents are…
This paper studies settings where the analyst is interested in identifying and estimating the average \emph{direct} causal effect of a binary treatment on an outcome. We consider a setup in which the outcome realization does not get…
A new matching method is proposed for the estimation of the average treatment effect of social policy interventions (e.g., training programs or health care measures). Given an outcome variable, a treatment and a set of pre-treatment…
Flexible machine learning tools are increasingly used to estimate heterogeneous treatment effects. This paper gives an accessible tutorial demonstrating the use of the causal forest algorithm, available in the R package grf. We start with a…
Dynamic prediction of causal effects under different treatment regimes conditional on an individual's characteristics and longitudinal history is an essential problem in precision medicine. This is challenging in practice because outcomes…
We study the problem of estimating the effect function for a continuous treatment, which maps each treatment value to a population-averaged outcome. A central challenge in this setting is confounding: treatment assignment often depends on…
Continuous Integration (CI) has evolved from a tooling strategy to a fundamental mindset in modern CI engineering. It enables teams to develop, test, and deliver software rapidly and collaboratively. Among CI services, GitHub Actions (GHA)…
In today's rapidly evolving landscape of automation and manufacturing systems, the efficient resolution of productivity losses is paramount. This study introduces a data-driven ensemble approach, utilizing the cyclic multivariate time…
Sequential multiple assignment randomized trials (SMARTs) are used to construct data-driven optimal intervention strategies for subjects based on their intervention and covariate histories in different branches of health and behavioral…
Background: Inference of gene regulatory networks from transcriptomic data has been a wide research area in recent years. Proposed methods are mainly based on the use of graphical Gaussian models for observational wild-type data and provide…
We introduce **SWE-Flow**, a novel data synthesis framework grounded in Test-Driven Development (TDD). Unlike existing software engineering data that rely on human-submitted issues, **SWE-Flow** automatically infers incremental development…
Background. Starting from the 1960s, practitioners and researchers have looked for ways to empirically investigate new technologies such as inspecting the effectiveness of new methods, tools, or practices. With this purpose, the empirical…
Graph machine learning has advanced rapidly in tasks such as link prediction, anomaly detection, and node classification. As models scale up, pretrained graph models have become valuable intellectual assets because they encode extensive…
Machine Learning (ML) applications are proliferating in the enterprise. Relational data which are prevalent in enterprise applications are typically normalized; as a result, data has to be denormalized via primary/foreign-key joins to be…
Software testing ensures the quality and reliability of software products, but manual test case creation is labor-intensive. With the rise of large language models (LLMs), there is growing interest in unit test creation with LLMs. However,…
The digitization of manufacturing processes enables promising applications for machine learning-assisted quality assurance. A widely used manufacturing process that can strongly benefit from data-driven solutions is gas metal arc welding…
Estimating causal effects in quasi-experiments with spatio-temporal panel data often requires adjusting for unmeasured confounding that varies across space and time. Gaussian Processes (GPs) offer a flexible, nonparametric modeling approach…
Temporally dense single-person "small data" have become widely available thanks to mobile apps and wearable sensors. Many caregivers and self-trackers want to use these data to help a specific person change their behavior to achieve desired…
The synthetic control method (SCM) is widely used for causal inference with panel data, particularly when the number of treated units is small. It relies on the stable unit treatment value assumption (SUTVA), ruling out spillover effects.…
Mediation analysis has traditionally focused on outcome-level summary contrasts, such as mean effects, which may obscure substantial distributional changes induced by complex and nonlinear causal mechanisms. We propose Distributional Causal…