Causal Inference in Science: Metaphysics and Methodology

Organisation: Zhitao Zhang & Alexander Gebharter

Time: 15:50 – 19:30, September 4, 2025

In philosophy of science, causality has become an increasingly popular topic. Many scientific claims and laws are stated in causal terms. For example, we say that a solar storm caused signal jamming. Though the laws of electromagnetism can be used to explain this phenomenon, it is usually the causal relation on which our actions are based—we monitor the solar activities because we know that solar storms can cause signal jamming (whatever the exact mechanism). On the other hand, causal relations are different from logical and statistical relations. Inference about causal relations is usually more complex and requires more specific background assumptions. Causality is deeply connected to other concepts such as correlation and manipulation. The discussions about causality and causal inference in science encompass diverse perspectives. This workshop’s focus lies on the intersection of metaphysics and methodology. Exemplary questions to be addressed are:

  • How should we understand and conceptualize causality? 
  • Which properties of the causal relation are relevant for science? 
  • To what extend do contemporary methods for causal inference track the metaphysical concept of causality and under which conditions do they succeed? 

For further information see the conference website: https://sophia-conference.org/

Speakers:

  • Elisa D’Olimpio (CPSP, Marche Polytechnic University)
  • Sepehr Ehsani (University College London)
  • Alexander Gebharter (CPSP, Marche Polytechnic University)
  • Stavros Ioannidis (National and Kapodistrian University of Athens)
  • Barbara Osimani (CPSP, Marche Polytechnic University)
  • Stathis Psillos (National and Kapodistrian University of Athens)
  • Julian Reiss (University of Linz)
  • Michał Sikorski (CPSP, Marche Polytechnic University)
  • Zhitao Zhang (University College London)

Program:

Time Room SR 1.004
15:50Opening
16:00Talk 1
16:35Talk 2
17:10Break
17:20Talk 3
17:55Talk 3
18:30Break
18:40Talk 5
19:15Closing

Titles & Abstracts:

Sepehr Ehsani: TBA

TBA

Alexander Gebharter, Barbara Osimani, Michał Sikorski, Elisa D’Olimpio, & Zhitao Zhang: A causal account for estimating effects across populations
when no causal information is available

The question of how experimental results obtained in a study population can be used to estimate causal effects in a target population is key in science and evidence-based decision making. This task becomes especially challenging in the presence of heterogeneity, i.e., when the values of some variables are distributed significantly differently among individuals in the two populations. In this paper, we develop an account for effect estimation across populations that combines causal models and Jeffrey conditionalization. We formulate a criterion for empirically testing whether the effect of an intervention in the target population can be estimated from experimental results obtained in the study population and argue that causal effects in the target population can be estimated without any prior knowledge of the underlying causal structure if the criterion is satisfied. This is a major advantage of the approach, as obtaining causal information is often difficult, particularly in policy-making contexts.

Stavros Ioannidis & Stathis Psillos: TBA

TBA

Julian Reiss: TBA

TBA

Zhitao Zhang: TBA

TBA

Project funded under the National Recovery and Resilience Plan (NRRP), Mission 4, Component 2, Investment 1.1 — Call for tender No. 1409/2022 of 14/09/2022 of the Italian Ministry of University and Research funded by the European Union – NextGenerationEU. Award Number: P2022RT4AT_001, Concession Decree No. 1371 of 01/09/2023 adopted by the Italian Ministry of University and Research, CUP I53D23006890001, Controlling and Utilizing Uncertainty in the Health Sciences.