References | Resources for Causal Reasoning in Health Services Research

From Association to Causation in Observational Studies: The Role of Tests of Strongly Ignorable Treatment Assignment. Rosenbaum Paul R. 2011
The Causal Foundations of Structural Equation Modeling. Pearl Judea. 2011
Attributable Fractions for Sufficient Cause Interactions. Vanderweele Tyler J. The International Journal of Biostatistics; 2010
Direct Effect Models. van der Laan Mark J., Petersen Maya L. The International Journal of Biostatistics; 2009

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Propensity score: Definition

Propensity score
the probability of treatment given baseline characteristics
Excess proportion: Definition

Excess proportion
a proportion of cases that would not have occurred had the population not been exposed, relative to cases when the entire population was exposed (i.e. counterfactual risk when exposed is compared with counterfactual risk when unexposed)
d-separation: Definition

d-separation
variables $X$ and $Y$ are said to be $d$ -separated, if all paths from $X$ to $Y$ are blocked. A path may be blocked by a set of variables $S$ , if the path contains at least one arrow-emitting variable that is in $S$ or if the path contains at least one collider that is not in S and has no descendent in $S$ . A collider is defined as a variable on a path such that the path enters and leaves via arrow heads. $X$ and $Y$ are, therefore, $d$ -separated conditional on $S$ , if $S$ blocks all paths between $X$ and $Y$ . X and Y are marginally independent when S contains the null set
Counterfactual reasoning: Definition

Counterfactual reasoning
comparing potential outcomes of alternative interventions with observed outcomes in a thought experiment

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