Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies

By Peter C. Austin, Elizabeth A. Stuart. 2015

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The propensity score is defined as a subject's probability of treatment selection, conditional on observed baseline covariates. Weighting subjects by the inverse probability of treatment received creates a synthetic sample in which treatment assignment is independent of measured baseline covariates. Inverse probability of treatment weighting (IPTW) using the propensity score allows one to obtain unbiased estimates of average treatment effects. However, these estimates are only valid if there are no residual systematic differences in observed baseline characteristics between treated and control subjects in the sample weighted by the estimated inverse probability of treatment. We report on a systematic literature review, in which we found that the use of IPTW has increased rapidly in recent years, but that in the most recent year, a majority of studies did not formally examine whether weighting balanced measured covariates between treatment groups. We then proceed to describe a suite of quantitative and qualitative methods that allow one to assess whether measured baseline covariates are balanced between treatment groups in the weighted sample. The quantitative methods use the weighted standardized difference to compare means, prevalences, higher‐order moments, and interactions. The qualitative methods employ graphical methods to compare the distribution of continuous baseline covariates between treated and control subjects in the weighted sample. Finally, we illustrate the application of these methods in an empirical case study. We propose a formal set of balance diagnostics that contribute towards an evolving concept of ‘best practice’ when using IPTW to estimate causal treatment effects using observational data. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Subfields: Statistics and Probability

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Citations in the corpus

Balanced Policy Evaluation and Learning (2017, methodology): “Based on the fact that 1 = E[⇡T (X)/'T (X)], a normalized IPW (NIPW) estimator is given by normalizing the weights so they sum to n, a common practice in causal effect estimation [2, 31]: ⌧̂NIPW(⇡) = ⌧̂WNIPW(⇡), W NIPW i (⇡) = W IPW i (⇡)/ Pn i0=1 W IPW i0 (⇡).”
On the Nuisance of Control Variables in Regression Analysis (2020, background): “Azoulay et al. employ an inverse probability weighting estimator (Austin and Stuart, 2015).”
An introduction to inverse probability of treatment weighting in observational research (2021, methodology): “by including interaction terms, transformations, splines) [24, 25].”
Covariate balancing propensity score by tailored loss functions (2016, background): “A widely used criterion is that a standardized difference above 10% is unacceptable (Austin and Stuart, 2015, Normand et al., 2001), which is the dashed line in Figure 1.”
Policy Evaluation with Latent Confounders via Optimal Balance (2019, background): “Since these weights can be extreme, both normalization [2, 31, 41] and clipping [10, 12, 40] are often employed.”
Demographics Should Not Be the Reason of Toxicity: Mitigating Discrimination in Text Classifications with Instance Weighting (2020, methodology): “For example, the Inverse Propensity Score (IPS) (Rosenbaum and Rubin, 1983) method has been successfully applied for causal effect analyses (Austin and Stuart, 2015), selection bias (Schonlau et al.”