Experimental Design & Causal Inference
Experimental design refers to the structured planning of experiments to investigate relationships between variables, ensuring valid and reliable results. It involves selecting subjects, assigning treatments, and controlling for confounding factors. Causal inference is the process of determining whether a relationship between variables is causal rather than merely correlational. Together, experimental design and causal inference enable researchers to draw robust conclusions about cause-and-effect relationships in scientific studies.
Experimental Design & Causal Inference
Experimental design refers to the structured planning of experiments to investigate relationships between variables, ensuring valid and reliable results. It involves selecting subjects, assigning treatments, and controlling for confounding factors. Causal inference is the process of determining whether a relationship between variables is causal rather than merely correlational. Together, experimental design and causal inference enable researchers to draw robust conclusions about cause-and-effect relationships in scientific studies.
💡 Key Takeaways
- Learn how randomized experiments (A/B tests) help isolate causal effects in software and data projects.
- Understand how to design experiments with proper randomization, control groups, blocking, and sample size planning to reduce bias.
- Grasp the basics of causal inference: potential outcomes, counterfactuals, and the difference between correlation and causation.
- Explore practical analysis methods in CS/data (propensity scores, matching, and other adjustments) and recognize common pitfalls like confounding and multiple testing.
❓ Frequently Asked Questions
What is experimental design and why is it important in CS & data?
Experimental design is the structured planning of experiments to test relationships between variables. It specifies how to select subjects, assign treatments, and control bias and confounding factors so results are valid and reliable.
What is randomization and why is it used?
Randomization assigns units to treatment or control by chance, reducing selection bias and balancing unknown confounders, which helps attribute observed differences to the treatment.
What is a confounding factor and how can you control it?
A confounder is a variable related to both treatment and outcome that can bias results. Control methods include randomization, blocking, matching, and statistical adjustment.
What is causal inference and what are common methods to estimate causal effects?
Causal inference aims to determine the effect of a treatment on an outcome. Common methods include randomized experiments, propensity-score matching, regression adjustment, instrumental variables, and regression discontinuity.