Exploring the technical aspects of causality
From the previous section, it is evident that causal inference involves employing observational or experimental data to establish causal links, utilizing various statistical methods and theories to measure the influence of one variable (the “treatment” or “intervention”) on another (the “outcome” or the “effect”).
From a statistical vantage point, it focuses on estimating the counterfactual, hypothesizing the outcomes in alternate scenarios where the treatment was absent. This necessitates assumptions about data and underlying mechanisms, including the exclusion of unmeasured confounders. Let’s go over these concepts one by one.
Counterfactual analysis
Counterfactual analysis involves exploring "what-if" scenarios to understand the effects of actions that didn't occur. It is used to estimate the causal impact of interventions by imagining alternative outcomes...
