Events

This workshop provides an introduction to causal inference using directed acyclic graphs (DAGs), a powerful framework that combines graph theory and probability to model and evaluate causal relationships. Originally developed in computer science and artificial intelligence, DAG-based methods are now widely used across disciplines such as economics, political science, sociology, health sciences, and philosophy. The course introduces the theoretical foundations of causal data science while emphasizing practical applications for empirical research. Participants will learn how to address common causal inference challenges – including backdoor adjustment, bad controls, instrumental variables, selection bias, and external validity – within a unified graphical framework. Through hands-on exercises and applications in R, the course equips participants with the tools to apply DAG-based causal reasoning and modern identification techniques in their own research projects. No formal prerequisites are required, although familiarity with basic statistics and R is beneficial.

Internal event

This paper examines how the twin transition relates to labor productivity by tracing the evolving regional demand for green and digital skills. We draw on more than 130 million Lightcast online job advertisements across ten European countries at the NUTS-3 level between 2014 and 2019, using the ESCO framework to classify green and digital skills. Regional skill-demand shares are combined with ARDECO productivity data and socioeconomic controls and analyzed using two-way fixed-effects and long-difference regressions, complemented by a shift-share instrumental-variable approach. We show that greening and digitalization are predominantly within-industry dynamics. While demand for digital skills is positively associated with regional labor productivity, demand for green skills is negatively associated with it. Observing that most green skills concern regulation rather than technologies, we explain the negative effects with organizational policy adjustment costs.

Contact person: Michael Rose

Subscription to the invitation mailing list and more information on the seminar page.

In this talk, I argue that Large Language Models (LLMs) offer social scientists a novel experimental tool: a digital “lab mouse.” Because LLMs encode human preferences, they make it possible to study aspects of human behavior computationally, at lower cost and greater scale than traditional human-subject experiments. LLMs also offer an unprecedented level of experimental control, which further enhances their appeal as a research tool. I will present results from several projects that: (a) establish this possibility for economic models, (b) develop applications across different social-scientific domains, and (c) highlight both the power and limitations of this approach.

Contact person: Marina Chugunova

Subscription to the invitation mailing list and more information on the seminar page.

Digitization is reshaping social, political, and economic relations and systems. Data-driven services and solutions have become essential to production, consumption and infrastructures. Central to these changes are digitized and digital value chains built around digital products and the extraction of data from multiple sources, including individuals. They operate in a field characterized by multiple actors (states, organizations, companies, individuals) embedded in multiple areas of law (including, but not only, contract, property, company, competition, IP, data, trade, environmental, human rights and international law) at different levels (regional, national, supranational, international). Law brings about and shapes these value chains.

For its 2nd Transnational Junior Faculty Forum, the German Law Journal and Max Planck Law invite extended abstracts (ca. 500 words) by early-career scholars investigating the law of digital value chains from across different fields of law. We especially invite contributions from the perspective of critical approaches, as well as research that works with methods from other disciplines. The extended abstracts should contain the research problem, theoretical or conceptual framework, description of perspective, outline of methods, and a prospective bibliography.

Please submit your abstract by 15 April 2026. Successful applicants will be notified by 16 May 2026. Articles are due by 23 August 2026.

There is no registration fee. Limited funding will be available for participants based outside Europe.

To register, please email Irina Domurath: irina.domurath(at)unipd.it
If you would like to attend without presenting a paper please email petersson(at)law.mpg.de to register.

This workshop, a staple of AOM since 2003, focuses on the empirical analysis of patent data, emphasizing the use of patents both as a measure of knowledge and innovation, and as an institutional phenomenon worthy of research in its own right. The goals of the PDW are twofold. First, it introduces participants to frontier developments in patent data and empirical approaches, including newly available data sources, advances in data linkage and AI-based methods, data limitations, and methods for effectively analyzing patent data. Second, it will provide examples of in-depth analyses using patent research. The panelists will engage in discussions with the audience to address the current state of patent research and future directions for research.

More information: Event entry at the Academy of Management Annual Meeting 2026

We investigate how negative product disclosure affects subsequent innovation. In 2019, the U.S. Food and Drug Administration (FDA) terminated an adverse event reporting exemption for a subset of medical device product markets and released 6 million previously withheld adverse events. We find that the rate of innovation in affected device markets declined by 17 percent, primarily driven by firms' responses to information about competitors' products rather than by the loss of ability to withhold information. The decline was attenuated in markets without close substitutes. Despite the overall reduction in innovation rate, firms introduced more incremental but safer and higher quality devices. Taken together, these findings show how large-scale negative product disclosure reshapes the allocation of innovative effort. 
By Colleen Cunningham (University of Toronto) and Jennifer Kao (UCLA)

Contact person: Elisabeth Hofmeister

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This paper studies how a major advance in artificial intelligence reshapes the production of scientific knowledge. I exploit the public release of AlphaFold2—a deep-learning system that predicts protein structures with near-experimental accuracy—as a sharp and field-wide reduction in the cost of structural information in structural biology. I construct a new protein-level dataset linking the universe of proteins to their experimental structural characterization, AlphaFold2 coverage, and the complete corpus of associated scientific publications. Following the release, proteins receiving larger informational shocks experience substantial increases in scientific activity: the probability of publication rises by 60–80 percent, and publication counts more than double relative to pre-release trends. These gains are not uniform. Publication responses are strongest for proteins that had partial, but incomplete, experimental structural information prior to AlphaFold2, indicating increasing returns to existing scientific capital. Rather than displacing experimentation, AlphaFold2 complements wet-lab research: experimental validation activity increases for proteins with high predicted coverage. The composition and organization of research teams also shift. Projects on high-coverage proteins involve more specialized and computationally oriented contributors, and resulting publications engage more intensively with structural and computational questions while maintaining experimental inquiry.

Contact Person: Michael Rose

Subscription to the invitation mailing list and more information on the seminar page.

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