An R package for parametric estimation of causal effects

Causality has been defined with the identification of the cause or causes of a phenomenon by establishing covariation of cause and effect, a time-order relationship with the cause preceding the effect, and the elimination of plausible alternative causes; see Shaughnessy et al. (2000). To claim a specific causal effect between two variables is quite a strong claim. First, there needs to be well-defined treatment and outcome with an established covariance. Second, the treatment must proceed the observed outcome. Third, there must be no other present confounders, i.e., other "treatments" that could have their own causal effect; see Judea (2010). While these conditions are not perfect parameters for inferring a causal relationship between a treatment and outcome, they help researchers remove strong bias from their studies; see Hammerton and Munafò (2021). A causal effect found in a causal inference study is almost never the true causal effect, rather a less-biased estimate that is significantly closer to the true causal effect of the treatment on the outcome. To calculate a true causal effect would require "counterfactual" outcomes that cannot be measured; see Judea (2010). To describe a counterfactual outcome, let us define some treatment Z and an outcome Y.