Causal Inference

STRATOS Initiative

Topic group 7 on `Causal Inference’ is a member of the STRATOS Initiative (STRengthening Analytical Thinking for Observational Studies), which is a large collaboration of experts in many different areas of biostatistical research. Ongoing research, discussions and activities within STRATOS are conducted to provide accessible and accurate guidance in the design and analysis of observational studies across nine topic groups and several cross-cutting panels. The guidance is intended for applied statisticians and other data analysts with varying levels of statistical education, experience and interest.

Background

The desire to draw causal inference from observed associations is age old. Simple association models have gradually gained in sophistication and their potential is typically well understood by practicing statisticians. Causal questions and answers on the other hand, need an extra dimension of abstraction which calls for special care and caution. The move from association to causation is by no means trivial and requires assumptions not only about the observed data structure, but also beyond the sampled data.

Aim

TG7 sets out to provide guidance on the sequence of steps involved in causal inference. This includes:

phrasing the causal question, defining the causal estimand
designing a sampling frame and/or selecting the observational data
formulating plausible assumptions for identification of the causal effect
justifying specific causal effect estimators
analyzing the data under the given assumptions, and conducting sensitivity analyses for untestable assumptions
interpreting and reporting results

Methods and Resources

To understand the causal structure and assumptions we are willing to impose on a data problem, the formalism of potential outcomes and/or causal diagrams can be very helpful. They can also point to estimators for the target parameter. Many different estimation techniques exist and the terminology includes besides causal graphs: augmented inverse probability weighting (with stabilized weights), doubly robust procedures, g-computation, marginal structural models, (robust) multiple imputation, matching, outcome regression standardization, propensity scores, principal stratification, and so forth.

On our website you will find documents for guidance, articles, datasets and examples of different data analyses.

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