-- BryanShepherd - 27 Dec 2017

Causal Inference (Spring 2018)

Instructor

Schedule

  • Lectures: Tuesday, Thursday, 10:30-12:00, Small Biostat Classroom

  • Office hours:

    • Bryan: 3:30-4:30pm Mondays, 2525 West End #11124

Other information

  • No textbook; we will primarily be reading journal articles. However, we will refer to the following textbooks fairly often:
    • Imbens GW, Rubin DB (2015). Causal Inference for Statistics, Social, and Biomedical Sciences. Cambridge University Press.
    • Pearl J (2009). Causality: Models, Reasoning, and Inference, Second Edition. Cambridge University Press.
    • VanderWeele TJ (2015). Explanation in Causal Inference: Methods for Mediation and Interaction.
  • The tentative lecture schedule is shown in the table below.
  • Students are expected to read the noted section in the text prior to each class.

Grading (tentative)

  • Class Participation
  • Homework
  • Exams

Lectures (tentative)

Date Lecture Topic Reading Slides Homework
Jan 9   Potential Outcomes; Rubin Causal Model Imbens GW, Rubin DB (2015). Causal Inference for Statistics, Social, and Biomedical Sciences. Cambridge University Press. Chapters 1, 3. Lecture1  
Jan 11   Rubin Causal Model

Holland PW (1986). Statistics and causal inference. JASA 81: 945-960.

Imbens GW, Rubin DB (2015). Causal Inference for Statistics, Social, and Biomedical Sciences. Cambridge University Press. Chapters 1, 3.

Pearl J (2009). Causality: Models, Reasoning, and Inference, Second Edition. Cambridge University Press. Section 11.4.5. (Causation without Manipulation!!!)
Lecture2
Jan 16   Causal Diagrams / Directed Acyclic Graphs

Greenland S, Pearl J, Robins JM (1999). Causal diagrams for epidemiologic research. Epidemiology 10: 37-48.

Hernan MA, Hernandez-Diaz S, Robins JM (2004). A structural approach to selection bias. Epidemiology 15: 615-625.

Lecture3a, Lecture3b  
Jan 18   Causal Diagrams and Identification of Causal Effects

Pearl J (1995). Causal diagrams for empirical research. Biometrika 82: 669-710.

Pearl J (2009). Causality: Models, Reasoning, and Inference, Second Edition. Cambridge University Press. Chapter 3.

Lecture4  
Jan 23   Causal Diagrams and Identification of Causal Effects (continued)

Pearl J (1995). Causal diagrams for empirical research. Biometrika 82: 669-710.

Pearl J (2009). Causality: Models, Reasoning, and Inference, Second Edition. Cambridge University Press. Chapter 3.

   
Jan 25   Propensity Scores

Joffe MM, Rosenbaum PR (1999). Invited commentary: propensity scores. American Journal of Epidemiology 150: 327-333.

Rosenbaum PR, Rubin DB (1983). The central role of the propensity score in observational studies for causal effects. Biometrika 70: 41-55.

Pearl J (2009). Causality: Models, Reasoning, and Inference, Second Edition. Cambridge University Press. Section 11.3.5. (Understanding Propensity Scores)

Lecture5a-RR1983, Lecture5b-JR-1999 Several DAGS (due Jan 30)
Jan 30   Propensity Scores in Practice

Kurth T, Walker AM, Glynn RJ, Chan KA, Gaziano JM, Berger K, Robins JM (2005). Results of multivariable logistic regression, propensity matching, propensity adjustment, and propensity-based weighting under conditions of nonuniform effect. American Journal of Epidemiology 163: 262-270.

Franklin JM, Eddings W, Austin PC, Stuart EA, Schneeweiss S (2017). Comparing the performance of propensity score methods in healthcare database studies with rare outcomes. Statistics in Medicine.

Austin PC, Stuart EA (2015). Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies. Statistics in Medicine 34: 3661-3679.

Lecture6a-KWG2005, Lecture6b-IPTW-standardizing-collapsibility  
Feb 1   Propensity Scores in Practice (continued)   Lecture7a-AS-2015, Lecture 7b-FEA2017, Lecture7c-PS-continuous.pptx  
Feb 6   Double Robustness

Bang H, Robins JM. Doubly robust estimation in missing data and causal inference models. Biometrics. 2005;61:962–972. [sections 1-2,5-6]

Kang JDY, Schafer JL. Demystifying double robustness: a comparison of alternative strategies for estimating a population mean from incomplete data. Statistical Science 2007; 22:523-539.

Lecture8

Apply 4 different PS approaches and a standard regression-based approach to estimate the causal effect of starting an NNRTI-based regimen vs. a PI-based regimen on time from ART initiation to death in simulated CCASAnet data.

Obtain a doubly robust estimator for the homework described above.

Perform a sensitivity analysis to investigate the sensitivity of the association seen in the earlier CCASAnet analysis on unmeasured confounding.

simple-simulated-data.zip

Due Feb 20.

Feb 8   BRYAN OUT OF TOWN Guest Lecture by Lucy D'Agostino McGowan
Lucy's Slides:
 
Feb 13   Sensitivity Analyses ! VanderWeele TJ, Ding P (2017). Sensitivity analysis in observational research: introducing the E-value. Annals of Internal Medicine.

Ding P, VanderWeele TJ (2016). Sensitivity analysis without assumptions. Epidemiology 2016; 27: 368-377.

Lecture9    
Feb 15   Mediation Analyses VanderWeele TJ (2015). Explanation in Causal Inference: Methods for Mediation and Interaction. Chapter 2, sections 2.1-2.6, 2.16, 2.10-2.15, 2.17-2.19. Appendix, sections A.2.1- A.2.2. Lecture10  
Feb 20   Mediation Analyses VanderWeele TJ (2015). Explanation in Causal Inference: Methods for Mediation and Interaction. Chapter 3, sections 3.2-3.4; Chapter 5, section 5.1.  

With the JOBS II dataset, perform a mediation analysis estimating the controlled direct effect, the natural direct effect, and the natural indirect effect of job training on (a) depressive symptoms considering job_seek as a mediating variable, and on (b) employment status considering the mediating variable job_seek. The JOBS II dataset can be found at https://dataverse.harvard.edu/dataset.xhtml?persistentId=hdl:1902.1/14801. This contains the data and analyses for

Imai K, Keele L, Tingley D. A general approach to causal mediation analysis. Psychological Methods 2010; 15: 309-334.

Code up the analyses suggested in Chapter 2 of VanderWeele, but compare and contrast these estimates with those obtained using the approach of Imai and colleagues.

Due March 1.

Feb 22   Compliance Angrist JD, Imbens GW, Rubin DB (1996). Identification of causal effects using instrumental variables. JASA 91:444-455 Lecture11  
Feb 27   Instrumental Variables Baiocchi M, Cheng J, Small DS (2014). Tutorial in biostatistics: instrumental variable methods for causal inference. Statistics in Medicine 33: 2297-2340. Sections 1-4, 5.1-5.2, 6-8, and 13-14. Lecture12

The dataset listed below contains results from a simulated randomized trial. Participants were assigned to treatment or control (assign) and their CD4 count was measured after 3 months of follow-up (cd4). The treatment they actually took is also recorded (trt), as is a baseline measure of the patients health status (health.status). Estimate the effect of assignment to treatment on CD4 (ITT effect), and the compliers average causal effect (CACE or LATE). Describe the assumptions made for estimation and contrast these estimates with more naive estimates such as the per protocol estimate and the as-treated estimate. Due March 13.

simulated-clinical-trial-data.csv

Mar 1   Principal Stratification

Frangakis CE, Rubin DB (2002). Principal stratification in causal inference. Biometrics 58: 21-29.

Gilbert PB, Bosch RJ, Hudgens MG (2003). Sensitivity analysis for the assessment of causal vaccine effects on viral loads in HIV vaccine trials. Biometrics 59; 531-541.

Shepherd BE, Gilbert PB, Mehrotra DV (2007). Eliciting a counterfactual sensitivity parameter. The American Statistician 61: 1-8.

Lecture13a, Lecture13b  
Mar 6   SPRING BREAK    
Mar 8   SPRING BREAK    
Mar 13   Principal Stratification – Goal or Tool?

Pearl J (2011). Principal stratification – a goal or a tool? International Journal of Biostatistics 7: 20.

!VanderWeele TJ (2011). Principal stratification – uses and limitations. International Journal of Biostatistics 7:28.

Joffe MM (2011). Principal stratification and attribution prohibition: good ideas taken too far. International Journal of Biostatistics 7: 35.

Sjolander A (2011). Reaction to Pearl’s critique of principal stratification. International Journal of Biostatistics 7: 22.
Lecture14

A clinical trial was performed to evaluate the effect of an intervention (spontaneous breathing treatment in the ICU) on cognitive function. Cognitive function is only measured on survivors.

1) Perform an analysis comparing cognitive function among survivors; what problems does this analysis have that make it hard to interpret this causally?

2) Perform an intention-to-treat type analysis where those who die are assigned poor cognitive function; what problems does this analysis have?

3) Perform a principal stratification analysis estimating the causal effect of intervention on cognitive function among those who would have survived to 3 months regardless of treatment assignment. Perform a sensitivity analysis under under SUTVA, randomization, and monotonicity; derive large sample bounds and estimate with a sensitivity parameter.

Data will be emailed to you and are not to be shared or posted.

Due April 3

Mar 15   Econometric Causality

Heckman JJ (2008). Econometric causality. International Statistical Review 76: 1-27.

Moscoe E, Bor J, Barnighausen T (2015). Regression discontinuity designs are underutilized in medicine, epidemiology, and public health: a review of current and best practice. Journal of Clinical Epidemiology 2015; 68: 132-143.

Lecture15a, Lecture15b  
Mar 20   Time-varying confounding Daniel RM, Cousens SN, De Stavola BL, Kenward MG, Sterne JAC (2012). Methods for dealing with time-dependent confounding. Statistics in Medicine 32: 1584-1618. Sections 1-4.   Reproduce Simple Example g-computation and IPW of MSM in Section 4; Reproduce simulations of section 5. Due April 3.
Mar 22   BRYAN OUT OF TOWN

Lesser-known estimands: ATM and ATO

Guest Lecture by Laurie Samuels

Li, L., & Greene, T. (2013). A weighting analogue to pair matching in propensity score analysis. The International Journal of Biostatistics, 9(2), 215–234.

Li, F., Morgan, K. L., & Zaslavsky, A. M. (2017). Balancing Covariates via Propensity Score Weighting. Journal of the American Statistical Association, 0(0), 1–11. (As of 16 Feb 2018, this one is available online, but has not been assigned to an issue.) http://www.tandfonline.com/doi/full/10.1080/01621459.2016.1260466

Laurie-Slides
Mar 27   BRYAN OUT OF TOWN    
Mar 29   BRYAN OUT OF TOWN    
Apr 3   Marginal structural models Daniel RM, Cousens SN, De Stavola BL, Kenward MG, Sterne JAC (2012). Methods for dealing with time-dependent confounding. Statistics in Medicine 32: 1584-1618. Sections 5, 7-9.    
Apr 5   Marginal structural models Daniel RM, Cousens SN, De Stavola BL, Kenward MG, Sterne JAC (2012). Methods for dealing with time-dependent confounding. Statistics in Medicine 32: 1584-1618. Sections 5, 7-9. Lecture16; simulation-code  
Apr 10   MSM

Robins JM, Hernan MA, Brumback B (2000). Marginal structural models and causal inference in epidemiology. Epidemiology 11: 550-560.

Hernan MA, Brumback B, Robins JM (2000). Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men. Epidemiology 11: 561-570.
Lecture17 Perform a marginal structural model analysis. Estimate the causal effect of HAART on mortality using the simulated HIV data attached below. Your analysis should be similar to that of Hernan, Brumback, and Robins (2000). More details are in the attached document. Due April 24. msm-assignment-instructions Simulated-HIV-data.zip
Apr 12   Dynamic Marginal Structural Models

Hernan MA, Lanoy E, Costagliola D, Robins JM (2006). Comparison of dynamic treatment regimes via inverse probability weighting. Basic and Clinical Pharmacology and Toxicology 98: 237-242.

Cain LE, Robins JM, Lanoy E, Logan R, Costagliola D, Hernan MA (2010). When to start treatment? A systematic approach to the comparison of dynamic regimes using observational data. International Journal of Biostatistics 6: 18.

Lecture18  
Apr 17   Applications of Dynamic Marginal Structural Models

Shepherd BE, Jenkins CA, Rebeiro PF, Stinnette SE, Bebawy SS, McGowan CC, Hulgan T, Sterling TR (2010). Estimating the optimal CD4 count for HIV-infected persons to start antiretroviral therapy. Epidemiology 21: 698-705.

Shepherd BE, Liu Q, Mercaldo N, Jenkins CA, Lau B, Cole SR, Saag MS, Sterling TR (2016). Comparing results from multiple imputation and dynamic marginal structural models for estimating when to start antiretroviral therapy. Statistics in Medicine 35: 4335-4351.
Lecture19  
Apr 19   BRYAN OUT OF TOWN Guest Lecture by Christina Tripp    
Apr 24   Targeted maximum likelihood estimation Schuler MS, Rose S (2017). Targeted maximum likelihood estimation for causal inference in observational studies. American Journal of Epidemiology 185: 65-73. replicating-Schuler-Rose-simulations.R  
Apr 26   Causal inference – never a dull topic

Dawid P (2000). Causal inference without counterfactuals, with Discussion. JASA 95: 407-448.

Hernan MA (2018). The C-word; scientific euphimisms do not improve causal inference from observational data. AJPH; 108: 616-619, with discussion.

   
Topic revision: r34 - 26 Apr 2018, BryanShepherd
 

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