Multiple (idealized) causal models must be integrated from different disciplinary approaches to adequately represent this complexity, including our uncertainty in predicting its behavior, which has implications for any social policy derived from these models.
In observational data settings, it is important to evaluate the extent to which a given identification strategy is consistent with multiple plausible causal models.
Many people hold multiple causal and treatment models of their illness [ 55].
We explore classical point forecast accuracy measures, explain why measures such as MAD, MASE and wMAPE are inherently unsuitable for count data, and use the randomized Probability Integral Transform (PIT) and proper scoring rules to compare the performances of multiple causal and noncausal forecasting models on two datasets of daily retail sales.
To address these issues, we recently proposed probabilistic fine-mapping methods (Hormozdiari et al., 2014; Kichaev et al., 2014) that build on the concept of a standard confidence interval by providing a well-calibrated, minimally sized confidence set of variants using principled, LD-aware modeling of multiple causal variants.
Although explicit models that allow for multiple causal SNPs that explain all the linkage in a region could be considered, difficulty arises if genotype effects and LD are modelled explicitly via the RR and δ parameters.
Although proponents of models further justify the use of multiple causal factors, they also acknowledge that their degrees of interactions are also complex (Qureshi 2008; Stringfellow 2010).
Indeed, the heterogeneity of disease etiology demands that causal models consider the simultaneous involvement of multiple genes.
In other words, these models are not statistically "identifiable" and multiple causal situations represented by different DAGs can map to a single set of independence assumptions.
