Well-Defined Interventions and Causal Variable Choice

Mitochondrial Haplogroups and Well Defined Populations

Interventions and Causal Inference

The literature on causal discovery has focused on interventions that involve randomly assigning values to a single variable. But such a randomized intervention is not the only possibility, nor is it always optimal. In some cases it is impossible or it would be unethical to perform such an intervention. We provide an account of “hard” and “soft” interventions, and discuss what they can contribut...

Reduction by well-defined objectives.

Examination of programmes of experimental work involving live animals indicates scope for worthwhile reduction in animal usage by identifying more clearly the objectives of the different stages of a programme. Having a defined first objective for new or transferred methodology as "to optimise the methods", rather than an exploratory "try it with a few animals and modify" approach, is more likel...

Well-Defined Stochastic Petri Nets

Formalisms based on stochastic Petri Nets (SPNs) can employ structural analysis to ensure that the underlying stochastic process is fully determined. The focus is on the detection of conflicts and confusions at the net level, but this might require to overspecify a given SPN model. The problem becomes even more critical when reward processes of interest derived from the basic underlying process...

Constraint-based causal discovery from multiple interventions over overlapping variable sets

Scientific practice typically involves repeatedly studying a system, each time trying to unravel a different perspective. In each study, the scientist may take measurements under different experimental conditions (interventions, manipulations, perturbations) and measure different sets of quantities (variables). The result is a collection of heterogeneous data sets coming from different data dis...