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The why of AI: Uncovering cause and effect in observational data

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    • Date
    18 February 2026
    Timeframe
    16:00 - 17:00 CET Geneva
    Duration
    60 minutes
    • Days
      Hours
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    Modern machine learning excels at identifying correlations. However, to make a real impact, we must understand causality, the “why” behind the data, and uncover the underlying causal mechanisms driving the observations. This is the core challenge addressed by causal discovery. This pursuit of causal understanding is foundational for the next generation of AI. It is the key to building genuinely explainable AI (XAI) that can justify its decisions with causal claims rather than just complex correlations. Furthermore, it is crucial for accelerating scientific progress, enabling researchers to unravel complex systems in fields ranging from medicine to economics.

    Although identifying causal links traditionally requires experiments (interventions), this is often impossible, impractical, or unethical.

    The central challenge, therefore, is learning cause-and-effect from purely observational data. In this talk, after briefly surveying the field, this talk discusses recent advances in this area, focusing on the fundamental problem of distinguishing cause from effect (i.e., does X→Y or Y→X?) from bivariate data.

     

    Learning Objectives
    By the end of this session, participants will be able to:

    • Define the fundamental difference between observational data and interventional data, and why this distinction is critical.
    • Explain why standard machine learning models based on correlation often fail to support effective interventions or explainability.
    • Differentiate between simple statistical dependence and causal directionality.
    • Identify the specific challenges and limitations of inferring causality when randomized experiments are impossible or unethical.
    • Discuss modern methodological approaches used to determine the direction of dependence (i.e., distinguish “X causes Y” from “Y causes X”) in bivariate data.

     

    Recommended Mastery Level / Prerequisites:

    Basic knowledge of statistics and probabilistic machine learning.

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