Events

Title and abstract will follow soon.

Contact person: Elisabeth Hofmeister

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Contact person: Dominik Asam

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Contact person: Daehyun Kim

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Title and abstract will follow soon.

Contact person: Elisabeth Hofmeister

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Contact person: Ulrike Morgalla

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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

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Title and abstract will follow.

Contact person: Daehyun Kim

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Title and abstract will follow soon.

Contact person: Elisabeth Hofmeister

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Title and abstract will follow soon.

Contact person: Daehyun Kim

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How novel ideas are adopted and recognized is crucial to scientific progress, but not all ideas from all groups are equally recognized. This paper studies whether and how increasing gender diversity at universities may lead to greater inclusion and recognition of research traditionally associated with women. Leveraging the transitions to coeducation of 76 all-male universities and novel text-based measures of research content, we show that coeducation led to overall modest shifts toward female-associated research. This aggregate effect masks substantial heterogeneity across fields: in disciplines with higher early female representation, we observe a pronounced increase in female-associated research driven by both existing faculty and new entrants. Male-dominated fields, by contrast, exhibit little change or even declines in female-associated research, primarily due to changes in hiring practices. These findings highlight that while diversity can foster innovation, its effects may only be concentrated in areas already receptive to the new perspectives. (joint work with Drew Hendrickson and Francesca Truffa)

Contact person:  Marina Chugunova

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