Conceptual framework for investigating causal effects from observational data in livestock.

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.

Causal effect of prolificacy on milk yield in dairy sheep using propensity score.

In animal production, it is often important to investigate causal relationships among variables. The gold standard tool for such investigation is randomized experiments. However, randomized experiments may not always be feasible, possible, or cost effective or reflect real-world farm conditions. Sometimes it is necessary to infer effects from farm-recorded data. Inferring causal effects between variables from field data is challenging because the association between them may arise not only from the effect of one on another but also from confounding background factors. Propensity score (PS) methods address this issue by correcting for confounding in different levels of the causal variable, which allows unbiased inference of causal effects. Here the objective was to estimate the causal effect of prolificacy on milk yield (MY) in dairy sheep using PS based on matched samples. Data consisted of 4,319 records from 1,534 crossbred ewes. Confounders were lactation number (first, second, and third through sixth) and dairy breed composition (<0.5, 0.5-0.75, and >0.75 of East Friesian or Lacaune). The causal variable prolificacy was considered as 2 levels (single or multiple lambs at birth). The outcome MY represented the volume of milk produced in the whole lactation. Pairs of single- and multiple-birth ewes (1,166) with similar PS were formed. The matching process diminished major discrepancies in the distribution of prolificacy for each confounder variable indicating bias reduction (cutoff standardized bias = 20%). The causal effect was estimated as the average difference within pairs. The effect of prolificacy on MY per lactation was 20.52 L of milk with a simple matching estimator and 12.62 L after correcting for remaining biases. A core advantage of causal over probabilistic approaches is that they allow inference of how variables would react as a result of external interventions (e.g., changes in the production system). Therefore, results imply that management and decision-making practices increasing prolificacy would positively affect MY, which is important knowledge at the farm level. Farm-recorded data can be a valuable source of information given its low cost, and it reflects real-world herd conditions. In this context, PS methods can be extremely useful as an inference tool for investigating causal effects. In addition, PS analysis can be implemented as a preliminary evaluation or a hypothesis generator for future randomized trials (if the trait analyzed allows randomization).