Conceptual framework for investigating causal effects from observational data in livestock

Abstract

Understanding causal mechanisms among variables is critical to efficient management of complex biological systems such as animal agriculture production. The increasing availability of data from commercial livestock operations offers unique opportunities for attaining causal insight, despite the inherently observational nature of these data. Causal claims based on observational data are substantiated by recent theoretical and methodological developments in the rapidly evolving field of causal inference. Thus, the objectives of this review are as follows: 1) to introduce a unifying conceptual framework for investigating causal effects from observational data in livestock, 2) to illustrate its implementation in the context of the animal sciences, and 3) to discuss opportunities and challenges associated with this framework. Foundational to the proposed conceptual framework are graphical objects known as directed acyclic graphs (DAGs). As mathematical constructs and practical tools, DAGs encode putative structural mechanisms underlying causal models together with their probabilistic implications. The process of DAG elicitation and causal identification is central to any causal claims based on observational data. We further discuss necessary causal assumptions and associated limitations to causal inference. Last, we provide practical recommendations to facilitate implementation of causal inference from observational data in the context of the animal sciences.

Figure 1.

Hypothetical network illustrating connections between breed, parity, and prolificacy, and their combined effects on milk yield of dairy sheep, as adapted from Ferreira et al. (2017).

Figure 2.

Hypothetical network illustrating the effect of dry matter intake on reproductive performance of dairy cattle, as adapted from Wiltbank et al. (2006).

Figure 3.

Hypothetical causal network illustrating direct and indirect effects between weight performance and health indicators (A) in a beef feedlot production system, as adapted from Cha et al. (2017). (B) extends the graph in (A) to illustrate a hypothetical situation of lack of faithfulness due to cancelation of direct and indirect effects. Units of each direct causal effect are suppressed for simplicity.

Figure 4.

(A) Hypothetical network illustrating an inverted fork type of path configuration in the context of piglet behavior (Merk Manual Veterinary Manual Online at http://www.merckvetmanual.com/behavior/normal-social-behavior-and-behavioral-problems-of-domestic-animals/behavioral-problems-of-swine). (B) The same network is depicted where conditioning (represented in gray) on either aggressive behavior (D₃) or mortality (D₄) creates a spurious association between overcrowding (D₁) and mixing (D₂), represented by the double-headed dashed arrow line. (C) Scatterplot of groups of pigs that either showed aggressive behavior (•) or did not (°), characterized by animal density (expressed as number of piglets/m²) and level of mixing (expressed as number of litters mixed).

Figure 5.

Hypothetical networks illustrating observationally equivalent mechanisms of nutritional induced lameness in obese horses, adapted from Kienzle and Fritz (2013).