A year and a day ago, the Survey Statistics blog series launched with: “it is the people that make make survey statistics (and anything) great”. This past weekend, we got to celebrate wonderful people at Andrew Gelman’s 60-ish Birthday workshop.

Artist Sophie Gelman made the below:

Yair Ghitza gave a talk about Andrew’s influence on polling. Yair is Chief Scientist at Catalist and coauthor of excellent papers about MRP we’ve cited in this blog series: Ghitza and Gelman 2013 and Ghitza and Gelman 2020. The discussion after his talk included mention of Nate Cohn’s May 18, 2026 NYT article about weighting with “synthetic past vote”.

Let’s use our notation from “is a mismeasured X better than none at all ?” and “more adventures in mismeasured X” (see also “more on recalled vote”):

• Y = current support
• X = true 2024 vote, unknown
• X* = recalled 2024 vote

And notation from “weights and MRP for voters”:

• V = current registered voter
• V2024 = record of voting in 2024 (a coarsened version of X that only tells of whether someone voted)

Suppose we want E(Y | V=1), current support among current registered voters. Using MRP, we might want to estimate this via E(E(Y | X, sample, V = 1) | V = 1). But we’ve got at least 2 challenges:

1. We can’t directly estimate E(Y | X, sample, V = 1) because we only have recalled vote X*.
2. We need p(X | V=1) for the outer expectation. But past election results give p(X).

Nate’s article proposes:

1. Create synthetic past vote, X**, which aims to improve recalled vote X*:
   1. Impute X** if X* is missing and there is a record they voted (i.e. V2024 = 1).
   2. Validate: set X** to “nonvoter” if there is no record they voted (i.e. V2024 = 0).
2. I interpret this to mean they estimate p(X** | V = 1) ? See “weights and MRP for voters” for ideas.
   

   “Synthetic past vote is weighted to match our estimate for how today’s registered voters …voted in the last election.”

    

What do you think ?