Creating a causal model
Decision-making will often involve understanding cause and effect. If the effect is desirable, you can decide to replicate its cause, or otherwise avoid it. You can change something on purpose to observe how it changes outcomes, or trace an accidental effect back to its cause, or simulate which change will produce the most beneficial impact. Causal inference can help us do all this by creating causal graphs and models. These tie all variables together and estimate effects to make more principled decisions. However, to properly assess the impact of a cause, whether by design or accident, you’ll need to separate its effect from confounding variables.
The reason causal inference is relevant to this chapter is that the bank’s policy decisions have the power to impact cardholder livelihoods significantly and, given the rise in suicides, even life and death. Therefore, there’s a moral imperative to assess policy decisions with the utmost...