Abstract #M81
Section: Breeding and Genetics
Session: Breeding and Genetics: Application and methods in animal breeding - Swine, poultry, and other species
Format: Poster
Day/Time: Monday 7:30 AM–9:30 AM
Location: Gatlin Ballroom
Session: Breeding and Genetics: Application and methods in animal breeding - Swine, poultry, and other species
Format: Poster
Day/Time: Monday 7:30 AM–9:30 AM
Location: Gatlin Ballroom
# M81
Inferring the causal effect of number of lambs born on milk yield in dairy sheep using propensity score methods.
Vera C. Ferreira*1, Bruno D. Valente1, David L. Thomas1, Guilherme J. M. Rosa1, 1University of Wisconsin-Madison, Madison, WI.
Key Words: causal inference, milk yield, prolificacy
Inferring the causal effect of number of lambs born on milk yield in dairy sheep using propensity score methods.
Vera C. Ferreira*1, Bruno D. Valente1, David L. Thomas1, Guilherme J. M. Rosa1, 1University of Wisconsin-Madison, Madison, WI.
Assigning causal interpretation to associations obtained from observational data is challenging as they are prone to confounding. Number of lambs born (prolificacy) in sheep may be considered as a potential factor contributing to milk yield (MY). However, inferring this effect using traditional regression or ANOVA techniques can generate spurious results whenever there are confounder variables that influence both the outcome (i.e., MY) and treatment (i.e., prolificacy). Propensity score (PS) methods tackle this issue by balancing baseline covariate distributions between treatment levels, allowing unbiased inference of marginal effects. This method belongs to the framework of causal models dealing with potential outcomes. It intends to mimic aspects of randomized trials, for which comparison of treatment groups is causally meaningful. Our goal was to infer the causal effect of ewe prolificacy on her subsequent MY using PS based on Matched Samples. Data comprised 4,319 records from 1,534 crossbred dairy ewes. The set of potential confounder variables was composed by lactation number (1st, 2nd, and 3rd – 6th) and dairy breed composition (<0.5, 0.5-0.75 and >0.75 of East Friesian or Lacaune). For the treatment variable, single lamb birth was assigned to Group 0, while multiple birth (2, 3 or 4 lambs) was assigned to Group 1. MY represented the volume of milk produced for the whole lactation (mean = 268.5 L and SD = 116.4 L). The analysis was conducted using the R package “nonrandom.” A total of 1,166 pairs of treated/nontreated individuals with similar PS values were formed. The criterion for similarity was defined by a caliper size equal to 20% of the sd in the PS logit (0.13) and a ratio of treated/untreated = 1. Standardized differences were chosen as the statistical test for the hypothesis of PS balance, and all covariates were deemed balanced after matching (cutoff for standardized bias = 0.2). The estimated causal effect of prolificacy on MY was 20.52 L, SE = 3.77 L, 95% CI = 13.13–27.91 L. Hence, results indicate that ewes that gave birth to a single lamb would be expected to have MY increased by 20.52 L if they had given birth to multiple lambs and all other variables were held constant.
Key Words: causal inference, milk yield, prolificacy