Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Speaker Deck
PRO
Sign in
Sign up
for free
G-methods for time-varying treatments (Causal inference: What if, Chapter 21-1)
Shuntaro Sato
November 25, 2020
Science
0
330
G-methods for time-varying treatments (Causal inference: What if, Chapter 21-1)
Keywords: 因果推論, Time-varying, G-formula, IP weighting, Doubly robust estimation
Shuntaro Sato
November 25, 2020
Tweet
Share
More Decks by Shuntaro Sato
See All by Shuntaro Sato
shuntaros
0
66
shuntaros
0
760
shuntaros
0
470
shuntaros
0
480
shuntaros
0
460
shuntaros
0
420
shuntaros
0
440
shuntaros
0
440
shuntaros
0
420
Other Decks in Science
See All in Science
shel
0
320
yuya4
3
450
diracdiego
0
110
y__mattu
1
430
smjyc
0
160
yuya4
3
800
sansandsoc
0
350
masso
8
1.4k
terensarnic
0
220
kamakiri1225
0
190
calpis10000
0
160
shel
0
290
Featured
See All Featured
frogandcode
128
20k
maltzj
502
36k
bermonpainter
343
26k
malarkey
119
16k
tenderlove
55
3.5k
scottboms
252
11k
lauravandoore
12
1.7k
shpigford
166
19k
ammeep
657
54k
destraynor
221
47k
marktimemedia
7
460
addyosmani
1346
200k
Transcript
None
・G-methods for time-fixed treatments 本日の内容 ・The g-formula for time-varying treatments
・IP weighting for time-varying treatments ・A doubly robust estimator for time-varying treatments
・G-methods for time-fixed treatments 本日の内容 ・The g-formula for time-varying treatments
・IP weighting for time-varying treatments ・A doubly robust estimator for time-varying treatments
Stratification effect measure modification (-) effect measure modification (+) Mantel-Haenszel
method 別々にオッズ比を報告
Why model? effect measure modification (-) effect measure modification (+)
別々にオッズ比を報告(1つの効果を報告できない) g-methods
g-formula A=1を代入 A=0を代入
IP weighting marginal structural model
Conditional or Marginal? outcome regression saturated parametric stratification g-formula IP
weighting g-estimation or algebraically equivalent
Time-varying treatment g-methods
・G-methods for time-fixed treatments 本日の内容 ・The g-formula for time-varying treatments
・IP weighting for time-varying treatments ・A doubly robust estimator for time-varying treatments
前提 ・本章ではidentifiability conditions(sequential exchangeability, positivity, and consistency)のviolationが ないものとする。 ・static treatment
strategies (always treat vs. never treat) の効果を推定する。
g-formula (weighted average) ・time-fixed treatment (A1 の反実アウトカム) ・time-varying treatment
g-formula (weighted average)
g-formula (weighted average)
g-formula (simulation) のシミュレーション と
g-formulaの注意点 ・DAGに基づいたcovariates L1 をモデルに含める ・static sequential exchangeabilityが成立すればstatic treatment strategyの効果はidentify可能
g-formulaの一般化 ・static treatment strategy ・dynamic treatment strategy linear regression logistic
regression
・G-methods for time-fixed treatments 本日の内容 ・The g-formula for time-varying treatments
・IP weighting for time-varying treatments ・A doubly robust estimator for time-varying treatments
IP weighting (weights) ・nonstabilized IP weights ・ stabilized IP weights
IP weighting (non-stabilized)
Stabilized weights non-stabilized weights: stabilized weights: Lと独立であればよい Lと独立であればよい
IP weighting (stabilized)
IP weightingの一般化 ・nonstabilized IP weights ・ stabilized IP weights logistic
regression logistic regression (misspecifiedでも可)
Marginal Structural Model ・2K > Nのときは推定できない ・marginal structural mean model
stabilized IP weightsを使って推定 misspecified??
Effect Measure Modification ・baseline variable VによるEMMがある場合、marginal structural modelは以下の通り(parametric) stabilized IP
weightsを使って推定 Vに入れて良いのはbaseline variableだけ
・G-methods for time-fixed treatments 本日の内容 ・The g-formula for time-varying treatments
・IP weighting for time-varying treatments ・A doubly robust estimator for time-varying treatments
Doubly Robust Estimator ・g-formula ・ IP weighting
1. Doubly Robust (time-fixed) 2. 3. A=1とA=0でそれぞれ を推定 を推定 ,
をLについて標準化
1. Doubly Robust (time-varying) 2. 3. を推定 からパラメータ を求める。 を求めておく
を推定し、Aの値に応じた を求める。 これを繰り返して を求める。 always treat
Discussion