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Causal inference with observational data
| Content Provider | Semantic Scholar |
|---|---|
| Author | Nichols, Austin |
| Copyright Year | 2007 |
| Abstract | Identifying the causal impact of some variables X on y is difficult in the best of circumstances, but faces seemingly insurmountable problems in observational data, where X is not manipulable by the researcher and cannot be randomly assigned. Nevertheless, estimating such an impact or “treatment effect” is the goal of much research, even much research that carefully states all findings in terms of associations rather than causal effects. I will call the variables X the “treatment” or treatment variables, and the term simply denotes variables of interest—they need not be binary (0/1) nor have any medical or agricultural application. Experimental research designs offer the most plausibly unbiased estimates, but experiments are frequently infeasible due to cost or moral objections—no one proposes to randomly assign smoking to individuals to assess health risks, or to randomly assign marital status to parents so as to measure the impacts on their children. Four types of quasi-experimental research designs offering approaches to causal inference using observational data are discussed below: |
| File Format | PDF HTM / HTML |
| DOI | 10.1177/1536867x0700700403 |
| Alternate Webpage(s) | http://pped.org/stata/ciwod.pdf |
| Alternate Webpage(s) | http://www-personal.umich.edu/~nicholsa/ciwod.pdf |
| Alternate Webpage(s) | https://ageconsearch.umn.edu/record/119292/files/sjart_st0136.pdf |
| Alternate Webpage(s) | https://doi.org/10.1177/1536867x0700700403 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
