Events

Firms rely on academic science and actively participate in the production of scientific knowledge. However, the impact of industry on academic science remains unclear. This study utilizes the site selection decisions of “Million Dollar Plants” (MDPs) to estimate the causal effects of industry on academic science. I compare the responses of scientists in counties that successfully attracted MDPs (“winners”) with those in counties that narrowly missed out on these MDPs (“runners-up”). The arrival of an MDP in a “winner” county shifts research of local scientists toward topics relevant to the firm, but not at the expense of either the quantity or quality of their work. This shift in research direction is not primarily driven by direct funding or collaboration. Instead, it occurs immediately after the announcement but before the physical establishment of these plants and is more likely to affect scientists without prior experience in commercialization. These findings indicate that scientists are refocusing their attention toward more applied and firm-relevant research.

Contact person: Elisabeth Hofmeister

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

Causal machine learning (Causal ML) is an emerging branch in ML/AI research aimed data-driven decision-making by integrating robust causal inference with advanced predictive algorithms. A key advantage of Causal ML is the ability to prediction under intervention, that is, to predict the outcomes of a treatment at the individualized level while adjusting for various confounders. Causal ML can explicitly model how the treatment impact varies across subpopulations, thus uncovering rich, individual-level heterogeneity that can be leveraged for personalized targeting and more effective decisions. In this talk, we explore the methodological foundations of Causal ML, discuss critical guardrails necessary for its rigorous and responsible deployment, and explore applications in behavioral science and policy. In particular, we introduce the “AI Heterogeneity Explorer”, which allows to uncover the differential effectiveness of behavioral interventions and thus identify for whom interventions are effective. The “AI Heterogeneity Explorer” provides a systematic recipe for understanding the heterogeneity of behavioral interventions, optimizing the personalized delivery of interventions, validating the targeting strategy—which offers a powerful alternative to one-size-fits-all approaches often used in data-driven decision-making. Finally, we illustrate how this explorer can be leveraged in the context of climate interventions to advance behavioral and climate science.

Contact person: Malte Toetzke

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

On 15/16 December 2025, the Max Planck Institute for Innovation and Competition will host the 8^th Research on Innovation, Science and Entrepreneurship Workshop (RISE8), an annual workshop for Ph.D. students and Junior Postdocs in Economics and Management. 

The goal of the RISE8 Workshop is to stimulate an in-depth discussion of a select number of empirical research papers. It offers Ph.D. students and Junior Postdocs an opportunity to present their work and to receive feedback.

Keynote speaker of the RISE8 Workshop is Matt Marx (Cornell University)

Get the Call for Papers RISE8.

For more information see RISE Workshop.