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Applied Statistics and Data Science Seminar on Monday April 07, 2025
April 7 @ 2:00 pm - 3:30 pm
Title: Understanding Causality: Rubin’s Potential Outcome Model and Philosophical Perspectives
Venue, time and date: ISRT, 2:00 pm, April 07, 2025
Speaker: Nahian Nujhat, Bushra Chowdhury, Md. Mutasim Billah, Faria Rauf Ria, Maliha Binte Alauddin, Institute of Statistical Research and Training, University of Dhaka.
Abstract:
Causal inference focuses on estimating cause-and-effect relationships, a key challenge in statistics, where distinguishing association from causation is crucial. In this talk, we will explore the foundations of causal inference through the lens of Paul W. Holland’s seminal 1986 Journal of the American Statistical Association paper “Statistics and Causal Inference.”
Rubin’s model formalizes causal effects through the potential outcomes framework, which requires observing both counterfactuals. The two potential outcomes for a unit refer to the outcome that would be observed if the unit receives the treatment and the outcome that would be observed if the unit does not receive the treatment. The fundamental problem of causal inference is–the impossibility of observing both potential outcomes for the same unit, which can be overcome under some untestable assumptions.
Several philosophers, such as Hume, Mill, and Suppes, have contributed to understanding causation. Hume emphasized that causation is observed through temporal succession, contiguity and constant conjunction rather than direct observation, which led him to be skeptical about causality. Mill believed that experimental inquiry is required to identify causal relationships. Suppes advanced the discussion by introducing a probabilistic theory of causality. These philosophical views are explored in the context of Rubin’s model.
Finally, we will address the question of what can be a cause, arguing that only manipulable factors can be considered causes in the context of experiments. This will lead to a discussion of the limitations of causal inference in observational studies and the importance of distinguishing between attributes and causes.
