Causal Effect Estimation with Observational Data: Methods and ...

Causal Effect Estimation with Observational Data: Methods and ...

Causal Effect Estimation with Observational Data: Methods and Applications Part II Michael Lamm and Yiu-Fai Yung SAS Institute 2018 Iowa and Nebraska SAS Users Groups C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d . Outline Part I Issues of causal inference from observational data Introducing the propensity score Theories and assumptions Matching methods

Part II Weighting methods Doubly robust methods Limitations Summary and conclusions C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d . Confounding Variables complicate the estimation of causal effect from an observational study Sports GPA (Academic Performance) Music (Music Training) Confounding variables are pretreatment characteristics associated with both the treatment and the outcome variables

Confounding variables explain parts of the observed treatment outcome association and can bias causal effect C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d . The propensity score is commonly used as the basis of matching methods No (Re-) Specify a propensity score model Good covariate balance? Yes Outcome analysis A propensity score is the probability of receiving treatment given : C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d . Causal effects are defined by using potential outcomes Potential outcomes are used to describe what outcome would occur for a subject under every possible treatment scenario potential outcome in the treatment condition potential outcome in the control condition You can estimate the ATE: E or the ATT: E

The stable unit treatment value assumption (SUTVA) ensures that causal effects are well-defined The consistency assumption relates the observed outcomes to the potential outcomes: No unmeasured confounding is assumed to enable the identification of treatment effects: C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d . Illustration of the Potential Outcomes Framework C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d . If you can observe all the potential outcomes ... Ob s Y(0 ) Y(1) Y(1) Y(0) 1 3 4 1

2 1 3 2 3 5 7 2 4 3 2 1 5 2 4 2

6 3 7 4 7 2 8 6 8 4 5 1 9 1 0 1

10 2 6 4 A hypothetical perfect sample in which you can observe all potential outcomes Y(1), Y(0) Average treatment effect = Mean(Y(1)) Mean(Y(0)) = 6 4 =2 C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d . The fundamental problem of causal inference Obs T Obs. Y(0) Obs. Y(1) 1 1

4 ? 4 2 0 1 1 ? 3 1 7 ? 7 4 0 3

3 ? 5 0 2 2 ? 6 0 3 3 ? 7 1 8 ?

8 8 1 5 ? 5 9 0 1 1 ? 10 0 2 2 ?

Holland (1986) Each Y indicates only one of the potential outcomes The other potential outcome is always missing Observed mean of (Y|T=0) = 2 Observed mean of (Y|T=1) = 6 Observed effect = 4 C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d . Propensity Scores Weighting Methods C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d . Inverse probability weighting can create a pseudo population with comparable treatment conditions Inverse probability weighting is a common approach for handling missing data A patient with covariates and treatment will have a weight based on the propensity score Observational studies have a large amount of missing potential outcomes

Only a single outcome is observed for each subject, so at least half of the potential outcomes are missing This is a much larger amount of missing data than typically encountered in an experiment of RCT C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d . Like experiments, observational studies should be carefully designed to ensure proper analyses Designing an experiment helps ensure that you are examining a well-defined causal question that satisfies the SUTVA What is the target population? When is treatment assigned and how long is the treatment period? Is the outcome being properly measured? The same questions should be considered when designing and analyzing observational studies Clear answers to design questions are necessary to state

C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d . Example 3. Propensity Scores Weighting Methods C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d . Does quitting smoking lead to weight change? Data: A subset (N=1,746) of NHANES I Epidemiologic Follow-Up Study (NHEFS) in Hernn and Robins (2016) Collect medical and behavioral information in an initial physical examination Follow-up interviews were done approximately 10 years later Treatment variable Quit: quit smoking during the 10-year period Outcome variable Change: change in weight (in kg) Confounders include: Activity, Age, BaseWeight, Education, Exercise, PerDay, Race, Sex, Weight, YearsSmoke

C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d . PROC PSMATCH: Estimating ATT through output weights proc psmatch data=smokingweight; class Sex Race Education Exercise Activity Quit ; psmodel Quit(Treated='1') = Sex Age Education Exercise Activity YearsSmoke output out= smokeATTWeights attwgt=attwgt; run; proc ttest data=smokeATTWeights; class Quit; var Change; weight attwgt; run; C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d . PerDay; ATT weights correct for bias and target the analysis to the population of interest For an individual with observed treatment and covariates , the ATT weight equals to

The propensity score is, For subjects in the treatment condition, ATT Weight = = 1 For subject in the control condition, ATT Weight = C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d . The output data set contains the original data, propensity scores, and ATT weights Obs sex age ... _PS_ attwgt 1 0 42

... 0.20604 0.25951 2 0 36 ... 0.16018 0.19073 3 1 56 ... 0.27455 0.37845 4

0 68 ... 0.46022 0.85260 5 0 40 ... 0.28227 0.39327 . . . ... .

. . . . ... . . . . . ... . . C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d . Inverse propensity score weighting method for estimating ATT T-test with weights based on the sample created from the PSMATCH procedure

Variable: Quit 0 1 Diff (1-2) Diff (1-2) Method Gpa Mean Pooled Satterthwaite 1.2495 4.2551 -3.2756 -3.2756 95% CL Mean 0.8253 3.6684 -4.0773 -4.2310 C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d . 1.6736 5.3818 -2.4740 -2.3203

Std Err 0.2162 0.4358 0.4087 0.4865 PROC CAUSALTRT: Estimating ATT with METHOD=IPWR proc causaltrt data=School method=ipwr att; class Sex Race Education Exercise Activity Quit ; psmodel Quit(Event='1') = Sex Age Education Exercise Activity YearsSmoke PerDay / plots = pscovden(effects(age YearsSmoke)); model gpa; run; The model statement and psmodel statement are both required when you use PROC CAUSALTRT You use the method= option to select an estimation method By default PROC CASUALTRT estimates the ATE, to you request estimation of the ATT you use the att option C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d . Inverse propensity score weighting method for estimating ATT Estimation of ATT by the IPWR method of the CAUSALTRT procedure Analysis of Causal Effect Parameter POM POM ATT

Treatment Level 1 0 Estimate Robust Std Err 4.5251 1.2495 3.2756 0.4352 0.2565 0.4815 Wald 95% Confidence Limits 3.6720 0.7467 2.3319 C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d . 5.3781 1.7522 4.2193 Z

Pr > |Z| 10.40 4.87 6.80 <.0001 <.0001 <.0001 Which PS-Weighting method in the CAUSALTRT and PSMATCH procedures should you use? Both yield the same estimates of ATT in this example PROC CAUSALTRT produces standard error estimates that takes the estimation of propensity scores into account The catch: You must be certain that your propensity score model is correct C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d . CAUSALTRT or PSMATCH

Same theoretical foundations: Potential outcomes framework (Neyman 1923; Rubin 1974) Some overlap in functionalities (e.g., weighting methods) PROC PSMATCH motto: Do not involve the outcome variables when you do propensity score analysis--stratification, matching or weighting Advantage: separation of design from analysis enables exploratory analysis in propensity score analysis PROC CAUSALTRT: Results from the propensity score model and outcome model can be combined--- AIPW or IPWREG Advantage: more efficient point estimates and more accurate standard error estimates C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d . Regression Adjustment and Doubly Robust Methods C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d . Methods for Causal Effect Estimation in PROC CAUSALTRT Treatment Model Outcome Model

No Weighting methods No Yes Yes Regression adjustment methods C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d . Doubly robust methods Regression Adjustment Method Estimation by regression adjustment performs the following steps: Fit models for the outcome separately within each of the treatment conditions

Compute predicted outcomes for each subject from these models Use the predicted values to estimate the treatment effect of interest METHOD=REGADJ option in PROC CAUSALTRT C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d . Considerations when using regression adjustment The method is dependent on a correctly specified outcome model Incorrectly specified outcome models can lead to biased model estimates and biased treatment effects Extrapolation might be a concern if covariate distributions in treatment conditions are systematically different C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d . Doubly Robust Methods of PROC CAUSALTRT Augmented inverse probability weighting Estimate the propensity score and perform weighing Augment weighting by using predicted outcome values

METHOD=AIPW in PROC CAUSALTRT Inverse probability weighted regression Estimate the propensity scores Fit outcome models with inverse probability weights Estimate the causal effects by using predicted values from METHOD=IPWREG in PROC CAUSALTRT C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d . Main Idea of Doubly Robust Methods You can get unbiased estimation of causal treatment effects if either or both of the following models that you specify are true: Propensity score model for the treatment variable Regression model for the outcome model Doubly robust: You have two chances to get it right AIPW formulas: Analytic formulas for computing standard errors (Lunceford &

Davidian 2004) C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d . Example 4. AIPW Example C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d . Estimating ATE by the AIPW Method proc causaltrt data=smokingweight method=aipw covdiffps plots=all; class Sex Race Education Exercise Activity Quit /descending; psmodel Quit = Sex Age Education Exercise Activity YearsSmoke PerDay; model Change = Sex Age Exercise Activity BaseWeight; run; The aipw estimation method requires models for both the treatment and the outcome You can request measures of covariate balance by using the covdiffps option C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d . Estimation of ATE by the AIPW Method Parameter POM POM ATE Treatment Level 1 0

Estimate Robust Std Err 5.0830 1.7781 3.3049 0.4495 0.2156 0.4911 Wald 95% Confidence Limits 4.2019 1.3556 2.3423 C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d . 5.9641 2.2007 4.2675 Z Pr > |Z| 11.31 8.25

6.73 <.0001 <.0001 <.0001 Assessing Covariate Balance Covariate Differences for Propensity Score Model Standardized Difference Unweighted Weighted Parameter Sex Sex Age Education Education Education Education Education Exercise Exercise Exercise Activity Activity Activity YearsSmoke PerDay 1

0 5 4 3 2 1 2 1 0 2 1 0 Variance Ratio Unweighted Weighted -0.1603 -0.0200 0.9962 1.0006 0.2820 0.1660 -0.0270 -0.0472 -0.1116 0.0318

0.0111 0.0196 -0.0015 -0.0034 1.0731 1.4610 0.9167 0.9811 0.8498 0.9847 1.0268 1.0624 0.9994 0.9953 0.0568 0.0398 -0.0029 0.0166 1.0252 1.0119 0.9986 1.0049 0.0740 0.0268

-0.0074 0.0196 1.2182 1.0043 0.9796 1.0029 0.1589 -0.2167 0.0253 0.0027 1.1846 1.1679 1.0894 1.3323 C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d . Propensity Score Clouds C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d . Example 4. Limitations C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d . Family Aid and Child Development

A subset of data from the 1997 Child Development Supplement to the Panel Study of Income Dynamics (Hofferth et al. 2001; Guo and Fraser 2015) Treatment variable AFDC: Receiving welfare benefit Outcome variables Lwi: childs development, as measured by the age-normalized letter-word identification portion of the WoodcockJohnson Tests for Achievement N=1,003 children whose primary caregiver was less than 36 years old C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d . Other Variables Age: Age of the child in 1997 PcgAFDC:

Indicator for whether the childs primary caregiver received support from a public assistance program when the primary caregiver was between the ages of 6 and 12 PcgEd: Number of years of schooling for the childs primary caregiver Race: Indicator for whether the child is African-American Ratio: Ratio of family income to the poverty threshold in 1996 Sex: Indicator for whether the child is male C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d . Data Set data Children; input Sex Race Age datalines; 0 0 4 0.6089 12 0 0 0 12 0.4113 9 0 0 0 12 4.9965 12 0 1 0 6 1.0683 11 1 1 0 4 1.0683 11 1 0 0 4 3.1081 12 1 Ratio PcgEd PcgAFDC AFDC Lwi; 1 1 0 0 0 0

81 93 109 74 79 88 1 0 0 0 1 0 99 115 108 108 101 79 ... more lines ... 1 0 0 1 1 0 1 1

1 1 1 1 6 5 4 3 3 5 0.7390 1.1932 1.5719 1.1919 0.3129 2.3229 12 12 11 12 9 12 1 1 1 1 0 0

; C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d . Estimating Welfare Effect on Child Development proc causaltrt data=Children covdiffps nthreads=2; class AFDC PcgAFDC Race Sex; psmodel AFDC(ref='0') = Sex Race Age PcgEd PcgAFDC/ plots=(pscovden weightcloud); model Lwi = Sex PcgEd Ratio; bootstrap seed=1776; run; C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d . AIPW Estimation of ATE by PROC CAUSALTRT Analysis of Causal Effect Parameter POM POM ATE Parameter POM POM ATE Treatment Level 1 0

Treatment Level 1 0 Estimate Robust Std Err Bootstrap Std Err 98.5565 103.14 -4.5867 1.4458 0.8086 1.6437 2.1344 0.7919 2.2470 Bootstrap Bias Corrected 95% Confidence Limits 94.5800 101.74 -8.8993

103.17 104.64 0.4247 Wald 95% Confidence Limits 95.7229 101.56 -7.8082 Z Pr > |Z| 68.17 127.56 -2.79 <.0001 <.0001 0.0053 C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d . 101.39 104.73 -1.3652 Assessing Covariate Balance Covariate Differences for Propensity Score Model Standardized Difference

Unweighted Weighted Parameter Sex Sex Race Race Age PcgEd PcgAFDC PcgAFDC 0 1 0 1 0 1 Variance Ratio Unweighted Weighted 0.0335 -0.0433 1.0036 0.9936

-0.9343 -0.0621 0.7404 0.9989 0.2196 -0.9067 -0.6476 0.0020 -0.0974 -0.0660 1.0266 0.6789 1.8658 0.9650 0.5439 1.0739 C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d . Covariate Densities C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d . Other Types of Propensity Score Models

PROC PSMATCH uses logistic regression models for estimating propensity scores If a propensity score model does not lead to good covariate balance, what can you do? Use another set of predictors in the logistic model Use another type of model C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d . Can another modeling techniques yield better propensity scores? /* Use of HPSPLIT to fit PSMODEL */ proc hpsplit data=children seed=12345 ; class AFDC PcgAFDC Race Sex; model AFDC = Sex Race Age PcgEd PcgAFDC; output out=smpred; id AFDC PcgAFDC Race Sex Age PcgEd Lwi; run; A decision tree that uses the same covariates offers a non-parametric alternative for predicting the propensity scores C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d . HPSPLIT Output Data Set

Obs 1 2 3 4 5 6 7 8 9 10 AFDC Pcg AFDC Race Sex 1 1 0 0 0 0 1 0 0 0

0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 1 1 0 0 0 1 1

Age Pcg Ed Lwi _Node_ 4 12 12 6 4 4 7 6 4 11 12 9 12 11 11 12 12 14 14 12

81 93 109 74 79 88 95 140 84 99 6 9 6 10 10 6 12 4 4 6 More lines C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d . _Leaf_ 1 3 1 4 4

1 6 0 0 1 P_AFDC0 P_AFDC1 0.92088 0.28571 0.92088 0.75000 0.75000 0.92088 0.59633 0.79070 0.79070 0.92088 0.07912 0.71429 0.07912 0.25000 0.25000 0.07912 0.40367 0.20930 0.20930 0.07912

Full matching with propensity scores from HPSPLIT proc psmatch data=smpred; class afdc; psdata treatvar=afdc(treated='1') ps=p_afdc1; match method=full(kmax=5) stat=ps caliper=. ; assess ps var=(age PcgEd) / plots=all weight=matchatewgt; output out=fullMatchTree matchid=_MID_ matchatewgt=atewgt; run; You specify the psdata statement when the input data set includes precomputed propensity scores The treatvar= option identifies the treatment variable The ps= option identifies the variable that contains the propensity scores C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d . No improvement is seen in the standardized mean differences Assessing balance of categorical covariates of the data set created by full matching proc freq data=fullMatchTree; table afdc*Sex afdc*race afdc*pcgAFDC; weight atewgt; run; C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d . Distribution of Sex and Race for the treatment conditions C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d .

Distribution of PcgAFDC for the treatment conditions C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d . Estimation of the ATE proc ttest data=fullMatchATE; class afdc; var lwi; weight atewgt; run; Variable: AFDC 0 1 Diff (1-2) Diff (1-2) Method Lwi Mean Pooled Satterthwaite 103.5 94.7856 8.7242 8.7242 C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d .

95% CL Mean 102.3 93.0614 6.7855 6.6174 104.7 96.5099 10.6629 10.8309 Limitations of the Study What about SUTVA? AFDC might have very different levels for families What about the positivity assumption? High-income families would not receive welfare What about the no unmeasured confounding assumption? Both

propensity score models fail to achieve covariate balance C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d . Summary of causal effect estimation Confounding in observational studies must be dealt with Under the potential outcomes framework, causal interpretations of the effects are possible if assumptions are satisfied You can use various methods for estimating average treatment effect (ATE) and average treatment effect for the treated (ATT) Assessing covariate balance is important C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d . Introductory Books and Articles Austin, P. C. (2011). An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies. Multivariate Behavioral Research 46:399424.

Hernn and Robins (2016). Causal Inference. Boca Raton, FL: Chapman & Hall/CRC. Forthcoming. Guo, S., and Fraser, M. W. (2015). Propensity Score Analysis: Statistical Methods and Applications (2nd ed.). Thousand Oaks, CA: Sage Publications. Morgan, S. L., and Winship, C. (2015). Counterfactuals and Causal Inference: Methods and Principles for Social Research (2nd ed.). New York: Cambridge University Press. Pan, W., and Bai, H (2015). Propensity Score Analysis: Fundamentals and Developments. New York: The Guilford Press. Stuart, E. A. (2010). Matching Methods for Causal Inference: A Review and a Look Forward. Statistical Science 25:121. SAS Global Forum Papers Lamm, M., and Yung, Y. F. (2017). Estimating Causal Effects from Observational Data with the CAUSALTRT procedure. In Proceedings of the SAS Global Forum 2017 Conference. Cary, NC: SAS Institute Inc. http://support.sas.com/resources/papers/proceedings17/SAS0374-2017.pdf

Yang, Y., Yung, Y. F. , and Stokes, M. (2017). Propensity Score Methods for Causal Inference with the PSMATCH Procedure. In Proceedings of the SAS Global Forum 2017 Conference. Cary, NC: SAS Institute Inc. http:// support.sas.com/resources/papers/proceedings17/SAS0332-2017.pdf Yung, Y. F., Lamm. M, and Zhang. W (2018). Causal Mediation Analysis with the CAUSALMED Procedure. In Proceedings of the SAS Global Forum 2017 Conference. Cary, NC: SAS Institute Inc. C o p y ri g h t S A S In st i tu te I n c . A l l ri g h ts re s e rv e d .

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