Book contents
- Frontmatter
- Contents
- Acknowledgments
- I INTRODUCTION
- II EXPERIMENTAL REASONING ABOUT CAUSALITY
- 2 Experiments and Causal Relations
- 3 The Causal Inference Problem and the Rubin Causal Model
- 4 Controlling Observables and Unobservables
- 5 Randomization and Pseudo-Randomization
- 6 Formal Theory and Causality
- III WHAT MAKES A GOOD EXPERIMENT?
- IV ETHICS
- V CONCLUSION
- References
- Author Index
- Subject Index
4 - Controlling Observables and Unobservables
Published online by Cambridge University Press: 05 June 2012
- Frontmatter
- Contents
- Acknowledgments
- I INTRODUCTION
- II EXPERIMENTAL REASONING ABOUT CAUSALITY
- 2 Experiments and Causal Relations
- 3 The Causal Inference Problem and the Rubin Causal Model
- 4 Controlling Observables and Unobservables
- 5 Randomization and Pseudo-Randomization
- 6 Formal Theory and Causality
- III WHAT MAKES A GOOD EXPERIMENT?
- IV ETHICS
- V CONCLUSION
- References
- Author Index
- Subject Index
Summary
Control in Experiments
Controlling Observables in Experiments
We begin our analysis of the Rubin Causal Model (RCM)-based approaches to estimating the effects of a cause with a review of those that work through the control of observable variables that can make it difficult to estimate causal effects. Specifically, using the notation of the previous chapter, there are two types of observable variables that can cause problems for the estimation of the effects of a cause, Zi and Xi. Recall that Yi is a function of Xi and Ti is a function of Zi. That is, Xi represents the other observable variables that affect our dependent variable besides the treatment variable and Zi represents the set of observable variables that affect the treatment variable. Moreover, these variables may overlap and we define Wi = Zi ∪ Xi.
In experiments researchers deal with these observable variables in two ways – through random assignment and through the ability to manipulate these variables as they do with treatment variables. In the next chapter we show how such random assignment sidesteps both observable and unobservable variables that can interfere with measuring the causal effect of the treatment.
But experimenters also can manipulate some of the observable variables that might have an effect on treatments or directly on voting behavior and thereby reduce their effects. For instance, one observable variable that might affect the treatment variable is the mechanism by which a voter learns the information.
- Type
- Chapter
- Information
- Experimental Political Science and the Study of CausalityFrom Nature to the Lab, pp. 101 - 140Publisher: Cambridge University PressPrint publication year: 2010