The importance of having a conceptual stage when reporting non-randomized studies

Bind, Marie‐Abèle; Rubin, Donald B.

doi:10.1080/24709360.2021.1913707

Cited by 3 publications

(2 citation statements)

References 29 publications

(23 reference statements)

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“…In all cases we start out with having an idea on how the outcome is related to the factor that we want to 6 Some authors indeed argue that all observational studies should be conceptually designed to mimic a hypothetical designed experiment of the issue that is investigated (Hernán 2016(Hernán , p. 676, 2018Bind and Rubin 2021;Moreno-Betancur 2021). Indeed, Bind and Rubin (2021) argue that it is necessary to mimic a conceptual designed experiment for "valid Fisherian or Neymanian inferences" (i.e., using "randomization-based p-values or confidence intervals"). Defining a hypothetical designed experiment for one's observational study is a good idea but does not lessen the need for conceptual definitions and substantive interpretations.…”

Section: The Potential Outcomes Framework Terminology and The Design Of (Natural) Experimentsmentioning

confidence: 99%

The casual effect of fertility: The multiple problems with instrumental variables for the number of children in families

Öberg¹

2021

Preprint

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Studies investigating how the number of children in a family affects the parents or the children face problems because the variable of interest is endogenous in the model. The currently accepted solution to this problem is to use instrumental variables (IVs), for example, based on twin births. In this paper, I review and add to the critique of IVs based on twin births and show that that there are so many issues—major and minor—with these IVs that results based on them are not reliable or interpretable. I also review other IVs used in the literature, for example IVs based on the sexes of the firstborn children, and conclude that there are, as of yet, no credible IVs for the number of children. We need to disregard results from studies applying these IVs, reevaluate the current state of knowledge, and develop new, more credible methods.

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Section: The Potential Outcomes Framework Terminology and The Design Of (Natural) Experimentsmentioning

confidence: 99%

The casual effect of fertility: The multiple problems with instrumental variables for the number of children in families

Öberg¹

2021

Preprint

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“…In this paper, we show how we can evaluate the extent to which studies exploiting wind directions as instrumental variables could be prone to the issues raised above. To achieve this goal, we follow the four consecutive stages of the causal inference pipeline proposed by 18 , 19 that explicitly embed the design of this type of observational study within an hypothetical randomized experiment 20 – 23 .…”

Section: Introductionmentioning

confidence: 99%

Improving the design stage of air pollution studies based on wind patterns

Zabrocki

Alari

Benmarhnia

2022

Sci Rep

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A growing literature in economics and epidemiology has exploited changes in wind patterns as a source of exogenous variation to better measure the acute health effects of air pollution. Since the distribution of wind components is not randomly distributed over time and related to other weather parameters, multivariate regression models are used to adjust for these confounding factors. However, this type of analysis relies on its ability to correctly adjust for all confounding factors and extrapolate to units without empirical counterfactuals. As an alternative to current practices and to gauge the extent of these issues, we propose to implement a causal inference pipeline to embed this type of observational study within an hypothetical randomized experiment. We illustrate this approach using daily data from Paris, France, over the 2008–2018 period. Using the Neyman–Rubin potential outcomes framework, we first define the treatment of interest as the effect of North-East winds on particulate matter concentrations compared to the effects of other wind directions. We then implement a matching algorithm to approximate a pairwise randomized experiment. It adjusts nonparametrically for observed confounders while avoiding model extrapolation by discarding treated days without similar control days. We find that the effective sample size for which treated and control units are comparable is surprisingly small. It is however reassuring that results on the matched sample are consistent with a standard regression analysis of the initial data. We finally carry out a quantitative bias analysis to check whether our results could be altered by an unmeasured confounder: estimated effects seem robust to a relatively large hidden bias. Our causal inference pipeline is a principled approach to improve the design of air pollution studies based on wind patterns.

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Propensity scores in the design of observational studies for causal effects

Rosenbaum

Rubin

2022

Biometrika

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Summary The design of any study, whether experimental or observational, that is intended to estimate the causal effects of a treatment condition relative to a control condition, refers to those activities that precede any examination of outcome variables. As defined in our 1983 article (Rosenbaum & Rubin, 1983), the propensity score is the unit-level conditional probability of assignment to treatment versus control given the observed covariates; so, the propensity score explicitly does not involve any outcome variables, in contrast to other summaries of variables sometimes used in observational studies. Balancing the distributions of covariates in the treatment and control groups by matching or balancing on the propensity score is therefore an aspect of the design of the observational study. In this invited comment on our 1983 article, we review the situation in the early 1980’s, and we recall some apparent paradoxes that propensity scores helped to resolve. We demonstrate that it is possible to balance an enormous number of low-dimensional summaries of a high-dimensional covariate, even though it is generally impossible to match individuals closely for all of the components of a high-dimensional covariate. In a sense, there is only one crucial observed covariate, the propensity score, and there is one crucial unobserved covariate, the ‘principal unobserved covariate’. The propensity score and the principal unobserved covariate are equal when treatment assignment is strongly ignorable, that is, unconfounded. Controlling for observed covariates is a prelude to the crucial step from association to causation, the step that addresses potential biases from unmeasured covariates. The design of an observational study also prepares for the step to causation: by selecting comparisons to increase the design sensitivity, by seeking opportunities to detect bias, by seeking mutually supportive evidence affected by different biases, by incorporating quasi-experimental devices such as multiple control groups, and by including the economist’s instruments. All of these considerations reflect the formal development of sensitivity analyses that were largely informal prior to the 1980s.

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The importance of having a conceptual stage when reporting non-randomized studies

Cited by 3 publications

References 29 publications

The casual effect of fertility: The multiple problems with instrumental variables for the number of children in families

The casual effect of fertility: The multiple problems with instrumental variables for the number of children in families

Improving the design stage of air pollution studies based on wind patterns

Propensity scores in the design of observational studies for causal effects

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