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Causal Inference and Decompositions for Sequence Data Using Generative Models

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Susan Athey (Stanford University)
Tuesday 27 January 2026, 11:00-12:00
Seminar Room 1, Newton Institute.

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CIFW05 - Causal Machine Learning for the Social Sciences

This talk will review recent work adapting tools from causal inference, including tools for estimating decompositions, average treatment effects, and heterogeneous treatment effects, to problems involving sequence data, such as sequences of words in text, sequences of jobs in worker careers, and sequences of measured behaviors and actions in customer journeys. We provide new theory tailored to these problems and apply the methods to the problem of estimating the gender wage gap in worker careers as well as decomposing the sources of changes in gender wage gaps over time. We illustrate approaches to derive insight about causal effects, including approaches to answer causal questions about how individual trajectories evolve over time.

This talk is part of the Isaac Newton Institute Seminar Series series.

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Causal Inference and Decompositions for Sequence Data Using Generative Models

📅 Download to calendar (vCal)

⚠️ Important: CIFW05 - Causal Machine Learning for the Social Sciences

👤 Speaker: Susan Athey (Stanford University)
📅 Date & Time: Tuesday 27 January 2026, 11:00 - 12:00
📍 Venue: Seminar Room 1, Newton Institute

Questions? Contact the organiser

Abstract

Series This talk is part of the Isaac Newton Institute Seminar Series series.

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Causal Inference and Decompositions for Sequence Data Using Generative Models

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Causal Inference and Decompositions for Sequence Data Using Generative Models

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Causal Inference and Decompositions for Sequence Data Using Generative Models

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Causal Inference and Decompositions for Sequence Data Using Generative Models

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