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Peng Ding’s contribution to the Discussion of ‘Assumption‐lean inference for generalised linear model parameters’ by Vansteelandt and Dukes. (2022). Ding, Peng.
In: Journal of the Royal Statistical Society Series B.
RePEc:bla:jorssb:v:84:y:2022:i:3:p:691-693.

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  2. Crump, R., Hotz, V.J., Imbens, G. & Mitnik, O. (2006) Moving the goalposts: Addressing limited overlap in the estimation of average treatment effects by changing the estimand. Technical report, National Bureau of Economic Research.

  3. Ding, P. (2021) The Frisch‐Waugh‐Lovell theorem for standard errors. Statistics & Probability Letters, 168, 108945.

  4. Freedman, D.A. (2008) Randomization does not justify logistic regression. Statistical Science, 23, 237–249.
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  5. Li, F. & Li, F. (2019) Propensity score weighting for causal inference with multiple treatments. Annals of Applied Statistics, 13, 2389–2415.
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  6. Li, F., Morgan, K.L. & Zaslavsky A.M. (2018) Balancing covariates via propensity score weighting. Journal of the American Statistical Association, 113, 390–400.

  7. Vansteelandt S. & Daniel, R.M. (2014) On regression adjustment for the propensity score. Statistics in Medicine, 33, 4053–4072.
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  8. Wallace, M.P. & Moodie, E.E.M. (2015) Doubly‐robust dynamic treatment regimen estimation via weighted least squares. Biometrics, 71, 636–644.

  9. Zhao, A. & Ding, P. (2022) Regression‐based causal inference with factorial experiments: estimands, model specifications, and design‐based properties. Biometrika, in press.
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