Causal Inference: Experiments & Quasi-Experiments
Causal inference is the process of determining whether a cause-and-effect relationship exists between variables. Experiments, such as randomized controlled trials, assign treatments randomly to eliminate bias and establish causality. Quasi-experiments, on the other hand, lack random assignment but use alternative methods—like matching or statistical controls—to approximate experimental conditions. Both approaches are essential in research for drawing valid conclusions about how interventions or changes impact outcomes.
Causal Inference: Experiments & Quasi-Experiments
Causal inference is the process of determining whether a cause-and-effect relationship exists between variables. Experiments, such as randomized controlled trials, assign treatments randomly to eliminate bias and establish causality. Quasi-experiments, on the other hand, lack random assignment but use alternative methods—like matching or statistical controls—to approximate experimental conditions. Both approaches are essential in research for drawing valid conclusions about how interventions or changes impact outcomes.
💡 Key Takeaways
- Distinguish experiments from quasi-experiments and what each reveals about causality.
- See how randomized controlled trials reduce bias through random treatment assignment.
- Explore common quasi-experimental designs (difference-in-differences, regression discontinuity, instrumental variables) and when to use them.
- Identify key validity threats (confounding, selection bias, measurement error) and basic mitigation strategies.
❓ Frequently Asked Questions
What is causal inference?
Causal inference is the process of determining whether a cause produces an effect, distinguishing true causation from mere correlation using study design and data.
How do experiments help establish causality?
Experiments randomly assign participants to receive or not receive a treatment, balancing confounders and isolating the treatment's effect; randomized controlled trials are the gold standard.
What are quasi-experiments?
Quasi-experiments aim to infer causality without random assignment by using real-world conditions or natural variations to compare treated and untreated groups, with methods to adjust biases.
What are common quasi-experimental designs?
Natural experiments, regression discontinuity, difference-in-differences, propensity score matching, and instrumental variable approaches are popular quasi-experimental designs used when randomization isn’t possible.