Causal Inference: A Statistical Paradigm for Inferring Causality
Inferring causation is one important aim of many research studies across a wide range of disciplines. In this chapter, we will introduce the concept of potential outcomes for its application to causal inference as well as the basic concepts, models, and a
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Causal Inference: A Statistical Paradigm for Inferring Causality Pan Wu, Wan Tang, Tian Chen, Hua He, Douglas Gunzler, and Xin M. Tu
Abstract Inferring causation is one important aim of many research studies across a wide range of disciplines. In this chapter, we will introduce the concept of potential outcomes for its application to causal inference as well as the basic concepts, models, and assumptions in causal inference. An overview of statistical methods for causal inference will be discussed.
1 Introduction Assessing causal effect is one important aim of many research studies across a wide range of disciplines. Although many statistical models, including the popular regression, strive to provide causal relationships among variables of interest, few
P. Wu () Value Institute, Christiana Care Health System, Newark, DE 19718, USA e-mail: [email protected] W. Tang Department of Biostatistics, School of Public Health & Tropical Medicine, Tulane University, New Orleans, LA 70112, USA e-mail: [email protected] T. Chen Department of Mathematics and Statistics, University of Toledo, Toledo, OH 43606, USA e-mail: [email protected] H. He Department of Epidemiology, School of Public Health & Tropical Medicine, Tulane University, New Orleans, LA 70112, USA e-mail: [email protected] D. Gunzler Center for Health Care & Policy, MetroHealth Medical Center, Case Western Reserve University, Cleveland, OH 44109, USA e-mail: [email protected] X.M. Tu () Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY 14642, USA e-mail: [email protected] © Springer International Publishing Switzerland 2016 H. He et al. (eds.), Statistical Causal Inferences and Their Applications in Public Health Research, ICSA Book Series in Statistics, DOI 10.1007/978-3-319-41259-7_1
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can really offer estimates with a causal connotation. A primary reason for such difficulties is confounding, observed or otherwise. Unless such factors, which constitute the source of bias, are all identified and/or controlled for, the observed association cannot be attributed to causation. For example, if patients in one treatment have a higher rate of recovery from a disease of interest than those in another treatment, we cannot generally conclude that the first treatment is more effective, since the difference could simply be due to different makeups of the groups such as differential disease severity and comorbid conditions. Alternatively, if those in the first treatment group are in better healthcare facilities and/or have easier access to some efficacious adjunctive therapy, we could also see a difference in recovery between the two groups. An approach widely used to address such bias in epidemiology and clinical trials research is to control for covariates in the analysis. Ideally, if one can find all confounders for the relationship of interest, differences found between treatment and control groups by correctly adjusting for such covariates do represent causal effects. However, as v
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