Causal inference [1] primarily concerns itself with understanding how one variable influences another. Before delving deeper into complex causal models, it is critical to precisely define the building blocks of causal reasoning: treatments, outcomes, and confounding. Treatments represent interventions or actions, outcomes are the effects we observe, and confounders (variables that influence both the treatment and the outcome, potentially biasing naive comparisons [2]). Confounders are variables that can obscure authentic causal relationships. Without clarity on these core concepts, attempts at causal analysis risk being misleading. This chapter develops these foundations systematically, combining intuition, formalism, and illustrative examples.