Bounding Causal Effects under Uncontrolled Confounding Using Counterfactuals

Download as .RIS

Citation

MacLehose, Richard F.; Kaufman, Sol; Kaufman, Jay S.; & Poole, Charles L. (2005). Bounding Causal Effects under Uncontrolled Confounding Using Counterfactuals. Epidemiology, 16(4), 548-555.

Abstract

Common sensitivity analysis methods for unmeasured confounders provide a corrected point estimate of causal effect for each specified set of unknown parameter values. This article reviews alternative methods for generating deterministic nonparametric bounds on the magnitude of the causal effect using linear programming methods and potential outcomes models. The bounds are generated using only the observed table. We then demonstrate how these bound widths may be reduced through assumptions regarding the potential outcomes under various exposure regimens. We illustrate this linear programming approach using data from the Cooperative Cardiovascular Project. These bounds on causal effect under uncontrolled confounding complement standard sensitivity analyses by providing a range within which the causal effect must lie given the validity of the assumptions.

URL

http://dx.doi.org/10.1097/01.ede.0000166500.23446.53

Reference Type

Journal Article

Year Published

2005

Journal Title

Epidemiology

Author(s)

MacLehose, Richard F.
Kaufman, Sol
Kaufman, Jay S.
Poole, Charles L.