Chapter 1 Lecture 01: Golems, Owls and DAGs
Golems:
Clay robots
Powerful
No wisdom or foresight
Dangerous
1.1 Limitation of Statistical models
Incredibly limiting
Focus on rejecting null hypotheses instead of research hypotheses
Relationship between hypothesis and test not clear
Industrial framework

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1.2 Null models rarely unique
1.3 Hypotheses and Models
Research requires more than than tiny null robots
Precise process model(s)
Statistical model (procedure, golem) justifed by implications of process model(s) and question (estimand)
1.4 Owls
“How to draw owls” is a metaphor that this couse will program the models by hands
1.4.1 Why drawing the Bayesian Owl
Understand what you are doing
Document you work, reduce error
Respectable scientific workflow
1.4.2 Five steps of
Theoretical estimand
Scientific causal models
Use 1 & 2 to build statistical models
Simulate from 2 to validate 3 yields 1
Analyze real data
1.4.3 Advantages of Bayesian Owl
Bayesian approach is permissive, flexible
Expresss uncertanty at all levels
Director solutions for measurement error, missing data
Focus on scientific modeling
1.5 DAGs (Directed Acylcic Graphs)
Bayes vs Frequentism
1.5.1 Science before statistics
For statistcal modesl to produce scientic insight, they require additional scoienctific modesl
The reasons for a statistical analuysis are not found in the data themselves, but rather in the causes of the data.
The causes of the data cannot be extracted from the data alone. No causes in; no causes out.
1.5.2 Causal inferences
More than association between variables
- Causal inference is prediction of intervention
Know a cause -> predict the consequences of an intervention
Causal inferences is imputation of missing observations
Know a cause -> construct unobserved counterfactural outcomes.
Each letter is a type of measure.
Arrows mean the causes
1.5.3 Tips
Which control variables
Absolute not safe to add everything
How to test the casual models
With more scientic knowlege, can do more
Golems: Brainless, powerful statistical models
Owls: Documented, objective procedures
DAGs: Transparent scientifc assumptions to justify scientifc efort expose it to useful critique connect theories to golems