Research Design, Causality, and Evidence Synthesis
Designing for Causal Inference
Why Causality Matters
Applied decisions depend on whether a change in outcome is caused by an intervention, context, or selection process. Causal inference requires careful design because random assignment is often impractical or unethical.
Design Types
Strengths and Limits of Common Designs
RCTs
- High internal validity
- May have limited generalizability
- Can be expensive and slow
Quasi-experiments
- More feasible in real settings
- More vulnerable to confounding
- Often crucial in policy evaluation
Evidence Synthesis
Meta-analysis integrates findings across studies, but the quality of the synthesis depends on study comparability, publication bias assessment, and attention to moderators such as population, dosage, and implementation fidelity.
Which design is most directly intended to approximate causal inference without random assignment?
Quasi-experiments exploit nonrandom variation to estimate causal effects as rigorously as possible.
Correct answer: Quasi-experiment
What is one major threat to causal interpretation in quasi-experimental research?
Without randomization, groups may differ in ways that also affect the outcome.
Correct answer: Confounding from preexisting group differences.
Evidence Appraisal Workflow
-
1
Step 1: Identify the research question and decision context.
-
2
Step 2: Evaluate design quality and risk of bias.
-
3
Step 3: Check sample characteristics and setting.
-
4
Step 4: Inspect effect sizes, heterogeneity, and robustness.
-
5
Step 5: Determine whether findings are portable to the target context.
Why is publication bias important in meta-analysis?
If null findings are underrepresented, pooled effects can be systematically overstated.
Correct answer: It can inflate estimated effects by preferentially including positive findings