Survey Methodology
Comments by Takumi SaegusaNote 1 on “Exchangeability assumption in propensity‑score based adjustment methods for population mean estimation using non‑probability samples”:

Causal inference, non-probability sample, and finite population

  • Release date: June 25, 2024
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Abstract

In some of non-probability sample literature, the conditional exchangeability assumption is considered to be necessary for valid statistical inference. This assumption is rooted in causal inference though its potential outcome framework differs greatly from that of non-probability samples. We describe similarities and differences of two frameworks and discuss issues to consider when adopting the conditional exchangeability assumption in non-probability sample setups. We also discuss the role of finite population inference in different approaches of propensity scores and outcome regression modeling to non-probability samples.

Key Words:  Causal inference; Finite population; Non-probability sample.

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How to cite

Saegusa, T. (2024). Comments on “Exchangeability assumption in propensity-score based adjustment methods for population mean estimation using non-probability samples”: Causal inference, non-probability sample, and finite population. Survey Methodology, Statistique Canada, n° 12‑001‑X au catalogue, vol. 50, n° 1. Paper available at http://www.statcan.gc.ca/pub/12-001-x/2024001/article/00006-eng.htm.

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ISSN : 1492-0921

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© His Majesty the King in Right of Canada, as represented by the Minister of Industry, 2024

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Catalogue No. 12-001-X

Frequency: Semi-annual

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