Speaker
Description
Policy debates, product decisions, and scientific claims all hinge on a simple question: what would happen to Y if we changed X? In this talk, I will present a practical causal inference workflow in R, using the marginaleffects package. This package offers a consistent interface for causal inference, and it is compatible with virtually all model-fitting packages in the R ecosystem. The key causal inference approach that we will focus on is called "G-computation," a flexible strategy that accommodates rich model specifications while producing estimates of easy-to-understand and useful quantities like the Average Treatment Effect, Average Treatment Effect on the Treated, or Conditional Average Treatment Effect. The talk includes brief, reproducible examples and emphasizes model-agnostic post-estimation tools that work with linear models, generalized linear models, and more flexible specifications. The audience will leave with a practical template for causal estimation and communication, plus a set of concise code patterns for common estimands.
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| Keywords: Please list up to 5 keywords to help us find the right session for your contribution. | causal inference, G-computation, marginaleffects, average treatment effect, counterfactuals |
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| Virtual Option | This submission is for onsite presentation only |
| Video Recording | Video sharing is fine |
| The author(s) agree(s) to take responsibility and be accountable for the contents of the submission and is/are authorized to present it. | Confirm |