5 Unconfoundedness – Causal Inference<br/>*The Remix*
In an earlier chapter, we introduced potential outcomes and noted that estimating average treatment effects was possible if the treatment had been randomized. Yet many important questions can’t be addressed with randomized controlled trials (RCTs). Imagine studying whether parental methamphetamine abuse causes child abuse. In theory, we could randomly assign families to methamphetamine use or not and observe the outcomes. But, of course, we can’t—and shouldn’t—run such an experiment. Not only would that experiment be unethical, but it’s also practically impossible. Yet the question remains vital. Observational or quasi-experimental designs step in to address causal questions when RCTs are unavailable. This isn’t because randomization wouldn’t answer the question—it absolutely would. Rather, it’s because the ideal experiment is often unethical, impractical, or outright impossible. But here’s the problem: when we move away from randomization, the standards for evidence often drop. That’s