Causality:A consulting and collaborating

statisticians perspective

Andrew Pickles

University of ManchesterESRC Methods Festival

Colour scheme by Amy Pickles

Non-causal Analysis

• Not all analysis need be concerned with formally identifying causes

- descriptive data still valuable

- establishing a prima facie case is often sufficient e.g. ‘adjusted’ regional/ethnic differences in unemployment

Evidence of Causality Evidence takes many forms:• theoretical plausibility, qualitative data and

experience, large sample historical/administrative data, observational studies, natural experiments, formally randomised experiments

• within each of these categories evidence for elaborations of the simple causal effect should be sought e.g. dose response

• The rank order is not accidental. Evidence from medicine shows increasing bias in effect estimates as the ‘rigour’ of the study decreases. Nonetheless replication across studies of different designs both within and across these categories is required.

Always Approximating

• We should not be attempting to model everything

• E.g. Rothman in epidemiology– typically concerned with component causes

• Non-focal causes often accounted for implicitly in the design or analysis

• Assumptions about non-focal causes should be as general as possible.

Misleading Simplification Response heterogeneity e.g. omitted interactions• Random coefficients to allow effects to vary with

unknown other factorse.g. effects of divorce (negative for some children, positive for others)But weak design can allow unjustified claims of ‘treatment works in some people’.

• Nature versus nurture debate – gene and environmental effects often represented by additive random effects, but many genetic effects are environmentally contingent and vice-versa

Randomisation and natural experiments

• Require strenuous effort and imagination• Protocol failures/departures require use of causal

analysis methods from observational studies:– Propensity score/marginal structural means/structural nested

models i.e. adjustment for known ‘confounders’– (Structural equation and random effects) modelling of

longitudinal data can account for some simple forms of ‘unmeasured’ confounders, and lagged/reciprocal effects under additional assumptions.

– Instrumental variables allow adjustment for unknown confounders (but hard to find convincing and efficient IV’s)