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The assumptions for valid causal inference outlined in Chapter 3 were conditional exchangeability (i.e. no unmeasured confounding), positivity (there must always be a positive probability of being exposed for every combination of confounders), no interference (no participant’s exposure status should influence that of another), and consistency (the meaning of being exposed should be the same across all of the exposed).

182 Conditional exchangeability cannot be assumed for the above analyses because several potential confounders were not used, including the alcohol environment, the influence of the extended family, and child abuse & neglect. However, while the thoroughness of the ESC-DAGs approach cannot account for unmeasured concepts, it does lend confidence to the position that whatever confounding bias might remain could be small. One extension to these analyses would be to use simulated data to try and quantify the strength of associations unmeasured confounders would need to have with any given exposure and the outcome to attenuate that exposure’s effect (Greenland, 1996).

The positivity assumption was generally not a problem for these analyses due to the ability to

investigate unbalanced propensity scores using Stata’s teffects suite, and respecify them. In other words, the programme allows the researcher to ensure that there is always a positive probability of a participant being exposed at each stratum of the propensity score.

A breach of the no interference assumption requires that that the parental influences on one adolescent measurably influence the parental influences of another adolescent. This could be problematic when adolescents live in the same neighbourhood or attend the same school, which is likely for at least a small number of participants given how ALSPAC is localised to the city of Bristol and its surrounds. It is not the case that the statistical methods used to account for neighbourhood and school effects would attenuate a breach of the no interference effect. Because interference results in clusters of highly correlated data, it could amplify effect sizes and artificially increase precision of estimates. However, this is perhaps more problematic for the peer effects than parental influences. Nevertheless, no interference may be an implausible assumption.

Consistency is the causal assumption that is most problematic for these analyses. Arguably, the consistency assumption is problematic in general when working with survey response data. For example, while the exposure of maternal drinking above 14 units per week is a clinically informed guideline, even this is not ‘consistent’ in the strictest sense. Recall that the definition of consistency means that the exposure should be ‘well defined’ such that it is the same for everyone. This is clearly not the case for this variable – drinking 14 units in one week could mean drinking all of them in one sitting or drinking 2 units per day (e.g. a glass of wine). Indeed, while these drinking patterns are considered harmful, their consequences may be characteristically different. Such issues are pervasive across the parental and intermediate variables used in the above analyses. As such consistency cannot be assumed in general. One potential extension to these analyses could be to perform sensitivity analysis on misclassification of the exposures (Greenland, 1996). While this would not be able to ascertain whether or not the exposure in question is ‘consistent’ in a technical sense, it could give an indication of how sensitive it is to exposed participants being mistakenly measured as unexposed and vice versa.

183 A causal interpretation of these estimates would require that each of these assumptions is met. As it is plausible that this is not the case, especially for conditional exchangeability and consistency, a causal interpretation may be overly ambitious. Thus, in terms of SRO 5, this thesis does not claim that ACEs are true causal effects. However, given the rigorous approach to confounder selection, the degree of sensitivity analysis, and the sophisticated statistical techniques used, estimates can still be argued to have a useful degree of external validity.

It is worth considering the underlying reasons for why the estimates produced in this chapter are not true causal effects - specifically, the use of ALSPAC as the sole data source for analysis. Several variables were not measured, and those that were tended not to meet the causal assumption of consistency. These are important observations. Firstly, ALSPAC is an excellent data source in a relative sense, especially in terms of the breadth of its measures. Thus, if ALSPAC data cannot be used for valid causal inference, this perhaps indicates that statistical methods may be advancing ahead of the ability to actually employ them in observational data. Secondly, when discussing data in a general sense, it is difficult to avoid the concurrently ubiquitous narrative on ‘big data’. Even if it is assumed that ALSPAC’s sample size is too small to qualify as ‘big data’, larger sample size is not a solution to problems of conditional exchangeability and consistency. As such, this chapter’s analyses suggest that the rhetoric surrounding big data is perhaps overly optimistic, at least in terms of

quantifying valid causal effects. As discussed in Chapter 7, the ideal data for this analysis would have repeated measures of all key variables. Short of this, a partial solution if perhaps unpragmatic

solution could be to use several data sources. Then the process of replacing the conceptual I-DAG with a data I-DAG could be repeated for each data source, as well as the subsequent analysis.