Bridging observational studies and randomized experiments by embedding the former in the latter

TL;DR

This paper introduces a four-stage strategy to estimate causal effects of environmental exposures from observational data by structuring it to resemble a randomized experiment, enhancing causal inference in health studies.

Contribution

It presents a novel framework combining conceptual, design, analysis, and summary stages to approximate randomized experiments from observational data, with practical diagnostics.

Findings

Applied to parental smoking and children's lung function in 1970s East Boston

Demonstrated the approach's ability to quantify exposure effects

Provided diagnostics to validate assumptions of the causal inference method

Abstract

The health effects of environmental exposures have been studied for decades, typically using standard regression models to assess exposure-outcome associations found in observational non-experimental data. We propose and illustrate a different approach to examine causal effects of environmental exposures on health outcomes from observational data. Our strategy attempts to structure the observational data to approximate data from a hypothetical, but realistic, randomized experiment. This approach, based on insights from classical experimental design, involves four stages, and relies on modern computing to implement the effort in two of the four stages.More specifically, our strategy involves: 1) a conceptual stage that involves the precise formulation of the causal question in terms of a hypothetical randomized experiment where the exposure is assigned to units; 2) a design stage that attempts to reconstruct (or approximate) a randomized experiment before any outcome data are observed, 3) a statistical analysis comparing the outcomes of interest in the exposed and non-exposed units of the hypothetical randomized experiment, and 4) a summary stage providing conclusions about statistical evidence for the sizes of possible causal effects of the exposure on outcomes. We illustrate our approach using an example examining the effect of parental smoking on children's lung function collected in families living in East Boston in the 1970's. To complement the traditional purely model-based approaches, our strategy, which includes outcome free matched-sampling, provides workable tools to quantify possible detrimental exposure effects on human health outcomes especially because it also includes transparent diagnostics to assess the assumptions of the four-stage statistical approach being applied.