class: center, middle, inverse, title-slide # Graphical representation of causal effects ## What If: Chapter 6 ### Elena Dudukina ### 2021-01-11 --- # Draw your assumptions before your conclusions - Causal diagrams - Directed acyclic graphs (DAGs)  --- # DAGs .pull-left[  ] .pull-right[  ] .footnote[Arah, O. A. 2008. “The Role of Causal Reasoning in Understanding Simpson’s Paradox, Lord’s Paradox, and the Suppression Effect: Covariate Selection in the Analysis of Observational Studies.” Emerg Themes Epidemiol 5 (February): 5. https://doi.org/10.1186/1742-7622-5-5] --- # DAGs  .footnote[Arah, O. A. 2008. “The Role of Causal Reasoning in Understanding Simpson’s Paradox, Lord’s Paradox, and the Suppression Effect: Covariate Selection in the Analysis of Observational Studies.” Emerg Themes Epidemiol 5 (February): 5. https://doi.org/10.1186/1742-7622-5-5] --- # When we observe a statistical association between two variables 1) Two variables are a cause and the consequence  -- 2) Two variables have a common cause (a confounder) - A and Y are associated even though A does not cause Y - Example: L - smoking, A - carrying a lighter, Y - lung cancer  --- # When we observe a statistical association between two variables 3) Two variables share a common consequence or a child of a common consequence, which was conditioned on - E and D are associated even though E does not cause D (or vice versa)   .footnote[Hernán, Miguel A., Sonia Hernández-Díaz, and James M. Robins. 2004. “A Structural Approach to Selection Bias.” Epidemiology 15 (5): 615–25] --- # Associatioin vs Causation - "Association, unlike causation, is a symmetric relationship between two variables; thus, when present, association flows between two variables regardless of the direction of the causal arrows" -- - Association is **not** causation --- # Marginal vs conditional probabilities - Conditional - The probability that an event Y occurs, given we know some other event A has occurred, is the conditional probability of Y given A - `\(Pr(Y|A)\)` - Marginal(unconditional) - `\(Pr(Y)\)` - Remembering standardization/adjustment formula - `\(Pr(Y^a)=\Sigma_lPr(Y=1|A=a, L=l)*Pr(L=l)\)` - Probability of PO `\(Y^a\)` marginal over confounder `\(L\)` .footnote[Causal Inference in Statistics: A Primer. Judea Pearl, Madelyn Glymour, Nicholas P. Jewell] ---  - `\(A \perp\perp Y|B\)` - `\(A \not\perp\not\perp Y\)` --- # Conditioning - `\(A \perp\perp Y|L\)`  - `\(A \not\perp\not\perp Y|L\)`   --- # Biasing path **For the association between A and Y** - A --> [L] <--Y   --- # Backdoor path - A <-- L --> Y  --- # Exchangeability, positivity, consistency ## inference is never assumption-free --- # Systematic bias - Infinite sample size won't help eliminating systematic bias - The magnitude of the effect is off - Structural definition of bias * Common causes --> **confounding**  * Conditioning on common effects --> **selection bias (collider stratification bias)**   * Information (measurement) error --> later in the book --- # Effect modification structure - Causal vs surrogate efect modifiers .pull-left[ ] .pull-right[  ] - Structure of the association of the effect modifier with the outcome matters --- ## Interaction in DAGs - Augmented DAGs <blockquote class="twitter-tweet"><p lang="en" dir="ltr">Right. Personally, I’ve found it useful to augment the DAG for heterogeneity as in these from my SER 2015 conference presentation: <a href="https://t.co/8QJ26BTx6Y">pic.twitter.com/8QJ26BTx6Y</a></p>— Onyi Arah🎄waiting for vaccine🌍 MD, DSc, PhD (@oacarah) <a href="https://twitter.com/oacarah/status/1169561608642863104?ref_src=twsrc%5Etfw">September 5, 2019</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>