Double Robustness in Estimation of Causal Treatment Effects
Double Robustness in Estimation of Causal Treatment Effects
Double Robustness in Estimation of Causal Treatment Effects
You also want an ePaper? Increase the reach of your titles
YUMPU automatically turns print PDFs into web optimized ePapers that Google loves.
Complication: Confound<strong>in</strong>g<br />
1. Introduction<br />
• The fact that a subject was exposed or not is associated with<br />
subject characteristics that may also be associated with his/her<br />
potential outcomes under treatment and control<br />
• To estimate the average causal effect from observational data<br />
requires tak<strong>in</strong>g appropriate account <strong>of</strong> this confound<strong>in</strong>g<br />
Challenge: Estimate the average causal treatment effect from<br />
observational data , adjust<strong>in</strong>g appropriately for confound<strong>in</strong>g<br />
• Different methods <strong>of</strong> adjustment are available<br />
• Any method requires assumptions ; what if some <strong>of</strong> them are wrong ?<br />
• The property <strong>of</strong> double robustness <strong>of</strong>fers protection aga<strong>in</strong>st some<br />
particular <strong>in</strong>correct assumptions. . .<br />
• . . . and can lead to more precise <strong>in</strong>ferences<br />
<strong>Double</strong> <strong>Robustness</strong>, EPID 369, Spr<strong>in</strong>g 2007 4
Change language