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Introduction to Causal Inference Approaches

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Book cover Health Services Evaluation

Part of the book series: Health Services Research ((HEALTHSR))

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Abstract

Many questions in health services research require causal estimates of the effects of policies or programs on a health outcome. Although randomized experiments are seen as the gold standard for estimating causal effects, randomization is often unfeasible and/or impractical or will not answer the question of interest. In those cases, rigorous nonexperimental study designs can be used, as highlighted in this chapter. The chapter first takes care to carefully define the causal effects of interest and stresses the importance of careful study design. Overviews of four common nonexperimental study designs are then provided: instrumental variables, regression discontinuity, interrupted time series (and the related approach of difference in differences), and propensity score matching. An emphasis is on applications of these methods in health services research and the assumptions underlying each approach. The chapter concludes with open topics and suggestions for the conduct of studies aiming to estimate causal effects in health services research.

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References

  • Andersson K, Petzold MG, Sonesson C, Lonnroth K, Carlsten A. Do policy changes in the pharmaceutical reimbursement schedule affect drug expenditures? Interrupted time series analysis of cost, volume, and cost per volume trends in Sweden 1986–2002. Health Policy. 2006;79:231–43.

    Article  PubMed  Google Scholar 

  • Angrist JD, Imbens GW. Two-stage least squares estimation of average causal effects in models with variable treatment intensity. J Am Stat Assoc. 1995;90(430):431–42. https://doi.org/10.1080/01621459.1995.10476535.

    Article  Google Scholar 

  • Angrist JD, Imbens GW. Identification of causal effects using instrumental variables. J Am Stat Assoc. 1996;91:444–55.

    Article  Google Scholar 

  • Baicker K, Finkelstein A. The effects of Medicaid coverage – learning from the Oregon experiment. N Engl J Med. 2011;365(8):683–5.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Bao Y, Duan N, Fox SA. Is some provider advice on smoking cessation better than no advice? An instrumental variable analysis of the 2001 National Health Iinterview Survey. Health Serv Res. 2006;41(6):2114–35.

    Article  PubMed  PubMed Central  Google Scholar 

  • Berger ML, Mamdani M, Atkins D, Johnson ML. Good research practices for comparative effectiveness research: defining, reporting and interpreting nonrandomized studies of treatment effects using secondary data sources: the ISPOR good research practices for retrospective database analysis task force report – part I. Value Health. 2009;12(8):1044–52.

    Article  PubMed  Google Scholar 

  • Bound J, Jaeger DA, Baker RM. Problems with instrumental variables estimation when the correlation between the instruments and the endogenous explanatory variable is weak. J Am Stat Assoc. 1995;90(430):443–50.

    Google Scholar 

  • Campbell SM, Reeves D, Kontopantelis E, Sibbald B, Roland M. Effects of pay for performance on the quality of primary care in England. N Engl J Med. 2009;361(4):368–78. https://doi.org/10.1056/NEJMsa0807651.

    Article  PubMed  CAS  Google Scholar 

  • Carneiro P, Heckman JJ, Vytlacil EJ. Estimating marginal returns to education. Am Econ Rev. 2011;101(6):2754–81.

    Article  PubMed  PubMed Central  Google Scholar 

  • Cole SR, Frangakis CE. The consistency statement in causal inference: a definition or an assumption? Epidemiology. 2009;20(1):3–5.

    Article  PubMed  Google Scholar 

  • Cook TD, Shadish WR, Wong VC. Three conditions under which experiments and observational studies produce comparable causal estimates: new findings from within-study comparisons. J Policy Anal Manage. 2008;27(4):724–50. https://doi.org/10.1002/pam.20375.

    Article  Google Scholar 

  • Crawford MJ, Thana L, Methuen C, Ghosh P, Stanley SV, Ross J, Gordon F, et al. Impact of screening for risk of suicide: randomized controlled trial. Br J Psychiatry. 2011;198(5):379–84.

    Article  PubMed  Google Scholar 

  • De Melo-Martín I, Sondhi D, Crystal RG. When ethics constrains clinical research: trial design of control arms in “greater than minimal risk” pediatric trials. Hum Gene Ther. 2011;22(9):1121–7.

    Article  PubMed  PubMed Central  Google Scholar 

  • Dowd BE. Separated at birth: statisticians, social scientists, and causality in health services research. Health Serv Res. 2011;46(2):397–420.

    Article  PubMed  PubMed Central  Google Scholar 

  • Durbin J. Testing for serial correlation in least-squares regression when some of the Regressors are lagged dependent variables. Econometrica. 1970;38(3):410–21.

    Article  Google Scholar 

  • Escarce JJ, Flood AB. Introduction to special section: causality in health services research. Health Serv Res. 2011;46(2):394–6. https://doi.org/10.1111/j.1475-6773.2011.01255.x.

    Article  PubMed  PubMed Central  Google Scholar 

  • Finkelstein EA, Fiebelkorn IC, Wang G. State-level estimates of annual medical expenditures attributable to obesity*. Obes Res. 2004;12(1):18–24. https://doi.org/10.1038/oby.2004.4.

    Article  PubMed  Google Scholar 

  • Fisher R. The arrangement of field experiments. Journal of Ministry of Agriculture. 1926;33:500–13.

    Google Scholar 

  • Frangakis CE, Rubin DB. Principal stratification in causal inference. Biometrics. 2002;58(1):21–9.

    Article  PubMed  PubMed Central  Google Scholar 

  • Frangakis CE, Rubin DB, An MW, MacKenzie E. Principal stratification designs to estimate input data missing due to death. Biometrics. 2007;63(3):641–9.

    Article  PubMed  Google Scholar 

  • Gluud LL. Bias in clinical intervention research. Am J Epidemiol. 2006;163(6):493–501. https://doi.org/10.1093/aje/kwj069.

    Article  PubMed  Google Scholar 

  • Goldberger A. Selection bias in evaluating treatment effects: some formal illustrations. In: Modelling and evaluating treatment effects in econometrics, Advances in econometrics. Bingley: Emerald Group Publishing Limited; 2008. p. 1–31.

    Google Scholar 

  • Greenland S. An introduction to instrumental variables for epidemiologists. Int J Epidemiol. 2000;29(4):722–9.

    Article  PubMed  CAS  Google Scholar 

  • Greenland S. Epidemiologic measures and policy formulation: lessons from potential outcomes. Emerging Themes in Epidemiology. 2005;2(1):5.

    Article  PubMed  PubMed Central  Google Scholar 

  • Hacker K, Penfold R, Zhang F, Soumerai SB. Impact of electronic health record transition on behavioral health screening in a large pediatric practice. Psychiatr Serv. 2012;63(3):256–61.

    Article  PubMed  Google Scholar 

  • Holland PW. Statistics and causal inference. J Am Stat Assoc. 1986;81(396):945–60.

    Article  Google Scholar 

  • Hughes JR. Ethical concerns about non-active conditions in smoking cessation trials and methods to decrease such concerns. Drug Alcohol Depend. 2009;100(3):187–93.

    Article  PubMed  Google Scholar 

  • Imai K, Keele L, Yamamoto T. Identification, inference and sensitivity analysis for causal mediation effects. Stat Sci. 2010;25(1):51–71.

    Article  Google Scholar 

  • Imai K, King G, Stuart EA. Misunderstandings between experimentalists and observationalists about causal inference. J R Stat Soc Ser A Stat Soc. 2008;171(2):481–502.

    Article  Google Scholar 

  • Imbens GW, Lemieux T. Regression discontinuity designs: a guide to practice. J Econ. 2008;142(2):615–35.

    Article  Google Scholar 

  • Lee BK, Lessler J, Stuart EA. Improving propensity score weighting using machine learning. Stat Med. 2010;29(3):337–46.

    PubMed  PubMed Central  Google Scholar 

  • Lehman DR, Wortman CB, Williams AF. Long-term effects of losing a spouse or child in a motor vehicle crash. J Pers Soc Psychol. 1987;52(1):218–31.

    Article  PubMed  CAS  Google Scholar 

  • Linden A, Adams JL. Evaluating disease management programme effectiveness: an introduction to instrumental variables. J Eval Clin Pract. 2006;12(2):148–54. https://doi.org/10.1111/j.1365-2753.2006.00615.x.

    Article  PubMed  Google Scholar 

  • Linden A, Adams JL, Roberts N. Evaluating disease management programme effectiveness: an introduction to the regression discontinuity design. J Eval Clin Pract. 2006;12(2):124–31.

    Article  PubMed  Google Scholar 

  • Linden A, Adams JL. Using propensity score-based weighting in the evaluation of health management programme effectiveness. J Eval Clin Pract. 2010;16(1):175–9.

    Article  PubMed  Google Scholar 

  • Little RJ, Rubin DB. Causal effects in clinical and epidemiological studies via potential outcomes: concepts and analytical approaches. Annu Rev Public Health. 2000;21:121–45. https://doi.org/10.1146/annurev.publhealth.21.1.121.

    Article  PubMed  CAS  Google Scholar 

  • Liu W, Kuramoto SK, Stuart EA. An introduction to sensitivity analysis for unobserved confounding in non-experimental prevention research. Prev Sci. 2013;14(6):570–80. PMCID:3800481.

    Article  PubMed  PubMed Central  Google Scholar 

  • Long SK, Coughlin T, King J. How well does medicaid work in improving access to care? Health Serv Res. 2005;40(1):36–58. https://doi.org/10.1111/j.1475-6773.2005.00341.x.

    Article  Google Scholar 

  • Ludwig J, Miller DL. Does head start improve children’s life chances? Evidence from a regression discontinuity design. Institute for the Study of Labor (IZA). 2006. Retrieved from http://ideas.repec.org/p/iza/izadps/dp2111.html

  • Marasinghe JP, Amarasinghe AAW. Noncompliance in randomized controlled trials [4]. CMAJ. 2007;176(12):1735.

    Article  PubMed  PubMed Central  Google Scholar 

  • Marcus SM, Stuart EA, Wang P, Shadish WR, Steiner PM. Estimating the causal effect of randomization versus treatment preference in a doubly randomized preference trial. Psychol Methods. 2012;17(2):244–54.

    Article  PubMed  PubMed Central  Google Scholar 

  • McClellan M, McNeil BJ, Newhouse JP. Does more intensive treatment of acute myocardial infarction in the elderly reduce mortality? Analysis using instrumental variables. JAMA. 1994;272:859–66.

    Article  PubMed  CAS  Google Scholar 

  • Mills TC. Time series techniques for economists. Cambridge: Cambridge University Press; 1990.

    Google Scholar 

  • Mullins CD, Abdulhalim AM, Lavallee DC. Continuous patient engagement in comparative effectiveness research. JAMA J Am Med Assoc. 2012;307(15):1587–8.

    Article  CAS  Google Scholar 

  • Neyman J. On the application of probability theory to agricultural experiments. Essay on principles. Stat Sci. 1923;5(4):465–80.

    Article  Google Scholar 

  • Neyman J. On the two different aspects of the representative method: the method of stratified sampling and the method of purposive selection. J R Stat Soc. 1934;97:558–606.

    Article  Google Scholar 

  • Oliver S, Armes DG, Gyte G. Public involvement in setting a national research agenda: a mixed methods evaluation. Patient Patient-Cent Outcomes Res. 2009;2(3):179–90.

    Article  Google Scholar 

  • O’Malley AJ. Commentary on Bryan Dowd’s paper “Separated at birth: statisticians, social scientists, and causality in health services research”. Health Serv Res. 2011;46(2):430–6.

    Article  PubMed  Google Scholar 

  • O’Malley AJ, Frank RG, Kaddis A, Rothenberg BM, McNeil BJ. Impact of alternative interventions on changes in generic dispensing rates. Health Serv Res. 2006;415(5):1876–94.

    Article  Google Scholar 

  • Pearl J. Statistics and causality: Separated to reunite – commentary on Bryan Dowd’s “Separated at birth”. Health Serv Res. 2011;46(2):421–9.

    Article  PubMed  PubMed Central  Google Scholar 

  • Peduzzi P, Wittes J, Detre K, Holford T. Analysis as-randomized and the problem of non-adherence: an example from the veterans affairs randomized trial of coronary artery bypass surgery. Stat Med. 1993;12(13):1185–95. https://doi.org/10.1002/sim.4780121302.

    Article  PubMed  CAS  Google Scholar 

  • Rosenbaum PR. Choice as an alternative to control in observational studies. Stat Sci. 1999;14(3):259–304.

    Article  Google Scholar 

  • Rosenbaum PR. Observational study. In: Everitt B, Howell D, editors. Encyclopedia of statistics in behavioral science. Chichester: Wiley; 2005a.

    Google Scholar 

  • Rosenbaum PR. Sensitivity analysis in observational studies. In: Everitt BS, Howell DC, editors. Encyclopedia of statistics in behavioral science, vol. 4. Chichester: Wiley; 2005b. p. 1809–14.

    Google Scholar 

  • Rosenbaum PR. Design of observational studies, Springer series in statistics. New York: Springer; 2010.

    Book  Google Scholar 

  • Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika. 1983;70(1):41–55.

    Article  Google Scholar 

  • Rosenberg L. Comparative effectiveness research: making it work for those we serve. J Behav Health Serv Res. 2009;36(3):283–4.

    Article  PubMed  Google Scholar 

  • Rothwell PM. External validity of randomised controlled trials? To whom do the results of this trial apply?? Lancet. 2005;365(9453):82–93.

    Article  PubMed  Google Scholar 

  • Rubin DB. The design versus the analysis of observational studies for causal effects: parallels with the design of randomized trials. Stat Med. 2007;26(1):20–36.

    Article  PubMed  Google Scholar 

  • Schneeweiss S. Sensitivity analysis and external adjustment for unmeasured confounders in epidemiologic database studies of therapeutics. Pharmacoepidemiol Drug Saf. 2006;15(5):291–303.

    Article  PubMed  Google Scholar 

  • Schneeweiss S, Rassen JA, Glynn RJ, Avorn J, Mogun H, Brookhart MA. High-dimensional propensity score adjustment in studies of treatment effects using health care claims data. Epidemiology. 2009;20(4):512–22.

    Article  PubMed  PubMed Central  Google Scholar 

  • Shadish WR, Cook TD, Campbell DT. Experimental and quasi-experimental designs for generalized causal inference. 2nd ed. Belmont: Wadsworth Publishing; 2002.

    Google Scholar 

  • Steiner PM, Cook TD, Shadish WR, Clark MH. The importance of covariate selection in controlling for selection bias in observational studies. Psychol Methods. 2010;15(3):250–67.

    Article  PubMed  Google Scholar 

  • Stuart EA. Matching methods for causal inference: a review and a look forward. Stat Sci. 2010;25(1):1–21.

    Article  PubMed  PubMed Central  Google Scholar 

  • Thistlethwaite DL, Campbell DT. Regression-discontinuity analysis: an alternative to the ex post facto experiment. J Educ Psychol. 1960;51(6):309–17.

    Article  Google Scholar 

  • Trochim W. Research design for program evaluation; the regression-discontinuity design. Beverly Hills: Sage; 1984.

    Google Scholar 

  • Wagenaar AC, Maldonado-Molina MM, Wagenaar BH. Effects of alcohol tax increases on alcohol-related disease mortality in Alaska: time-series analysis from 1976 to 2004. Am J Public Health. 2009;99(8):1464–70.

    Article  PubMed  PubMed Central  Google Scholar 

  • Werner RM, Konetzka RT, Stuart EA, Norton EC, Polsky D, Park J. Impact of public reporting on quality of Postacute care. Health Serv Res. 2009;44(4):1169–87. https://doi.org/10.1111/j.1475-6773.2009.00967.x.

    Article  PubMed  PubMed Central  Google Scholar 

  • Wong VC, Steiner PM, Cook TD. Analyzing regression-discontinuity designs with multiple assignment variables: a comparative study of four estimation methods. J Educ Behav Stat. 2012; https://doi.org/10.3102/1076998611432172.

    Article  Google Scholar 

  • Zaslavsky AM, Ayanian JZ, Zaborski LB. The validity of race and ethnicity in enrollment data for medicare beneficiaries. Health Serv Res. 2012;47(3 Part 2):1300–21.

    Article  PubMed  PubMed Central  Google Scholar 

  • Zimmerman M, Chelminski I, Posternak MA. Generalizability of antidepressant efficacy trials: differences between depressed psychiatric outpatients who would or would not qualify for an efficacy trial. Am J Psychiatr. 2005;162(7):1370–2.

    Article  PubMed  Google Scholar 

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Authors and Affiliations

  1. Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

    Elizabeth A. Stuart

  2. Behavioral Health Research and Policy, IBM Watson Health, Bethesda, MD, USA

    Sarah Naeger

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  1. Elizabeth A. Stuart
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  2. Sarah Naeger
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Correspondence to Elizabeth A. Stuart .

Editor information

Editors and Affiliations

  1. Community Health and Epidemiology, Dalhousie University, Halifax, NS, Canada

    Adrian Levy

  2. ICON plc, Vancouver, BC, Canada

    Sarah Goring

  3. Department of Biostatistics, Brown University, Providence, RI, USA

    Constantine Gatsonis

  4. University of British Columbia, Vancouver, BC, Canada

    Boris Sobolev

  5. European Observatory on Health Systems and Policies, Department of Health Care Management, Berlin University of Technology, Berlin, Germany

    Ewout van Ginneken

  6. Department Health Care Management Faculty of Economics and Management, Technische Universität Berlin, Berlin, Germany

    Reinhard Busse

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Stuart, E.A., Naeger, S. (2019). Introduction to Causal Inference Approaches. In: Levy, A., Goring, S., Gatsonis, C., Sobolev, B., van Ginneken, E., Busse, R. (eds) Health Services Evaluation. Health Services Research. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-8715-3_33

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  • DOI: https://doi.org/10.1007/978-1-4939-8715-3_33

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