INTRODUCTION:

The counterfactual or potential outcome model has become increasingly standard for causal inference in epidemiological, medical and nursing studies (M. Hofler 2005). In quantitative research, scholars attempt to arrive at valid counterfactuals by emulating an experimental design. However, because of treatments that are impossible to manipulate and the non-random assignment of data to treatment and control groups, causal statements are often based on invalid counterfactuals. In qualitative research, scholars attempt to arrive at valid counterfactuals by probing the historical and logical consistency of counterfactuals and by acknowledging the interconnectedness of events. Criteria to evaluate counterfactuals have been developed that allow for a discussion of the quality of counterfactuals used in causal statements (Patrick Emmenegger, 2011). The control group condition used as a basis of comparison in a study represents a proxy for the ideal counterfactual and is sometimes referred to as the counterfactual. Researchers have choices about what to use as the counterfactual and the decision has implications for interpreting the findings( Carmen G. Loiselle, Joanne Profetto-McGrath, Denise F. Polit, 2010).

Definition:

The condition or group used as a basis of comparison in a study embodying what would have happened to the same people exposed to a causal factor if they simultaneously were not exposed to the causal factor (Denise F. Polit, Cheryl Tatano Beck, 2008).

Counterfactual Theories of Causation:

The basic idea of counterfactual theories of causation is that the meaning of causal claims can be explained in terms of counterfactual conditionals of the form “If A had not occurred, C would not have occurred”.

The Counterfactual Model:

The core of the Counterfactual Model for observational data analysis is simple. Suppose that each individual in a population of interest can be exposed to two alternative states of a cause. Each state is characterised by a distinct set of condition exposure to which potentially effects an outcome of interest. In the Counterfactual tradition these alternative causal states are referred to as alternative treatment. When only two treatments are considered they are referred to as treatment and control. The key assumption of the Counterfactual framework is that each individual in the population of interest has a potential outcome under each treatment state even though each individual can be observed in only one treatment state at any point in time. (Stephen L. Morgan, Christopher Winship, 2007)

Counterfactual Possibilities in Nursing Research:

1. An Alternative intervention:

Subjects could receive two different types of distraction as alternative therapies.

2. A Placebo or Pseudo intervention:

Placebos are used to control for the non pharmaceutical effects of drugs such as the attention being paid to subjects.

3. Standard methods of care:

The usual procedures used to treat patients. This is the most commonly used control condition in nursing research.

4. Dose response effects:

Different doses or intensities of treatment wherein all subjects receive some type of treatment, but the experimental group gets treatment that is richer or more intense.

5. Wait – list control group:

Here the control group eventually receives the full experimental treatment but the treatment gets deferred.

6. Attention control group:

This type of control group is used especially if the primary control group receives no treatment or usual treatment and also if the researcher wants to find that the intervention effects are caused by the special attention given to the people receiving the intervention rather than the actual content of treatment.

CONCLUSION:

Counterfactuals are the basis of causal inference in medicine and epidemiology. Nevertheless the estimation of counterfactual differences poses several difficulties, primarily in observational studies. These problems, however, reflect fundamental barriers only when learning from observations and this does not invalidate the counterfactual concept. The nurse researcher should know how to incorporate counterfactuals in research. The formalization of counterfactuals is not a new discovery or even a new lesson, but rather an articulation of a concept that deserves more attention (or basic awareness) than it gets in health research. (Carl V Phillips and Karen J Goodman, 2006)

REFERENCES:

1. Patrick Emmenegger, (2011). How good are your counterfactuals? Assessing quantitative macro-comparative welfare state research with qualitative criteria. Journal of European Social Policy. vol. 21 no. 4 365-380.

2. M. Hofler, (2005). Causal inference based on counterfactuals. BMC Medical Research Methodology, 5:28

3. Stephen L. Morgan, Christopher Winship, 2007. Counterfactuals and Causal Inference: Methods and Principles for Social Research (Analytical Methods for Social Research)

4. Polit and Beck, (2008) “Nursing research- Generating and assessing evidence for nursing practice”, (8^th ed). Philadelphia: Lippincott Publishers, 252, 751.

5. Carl V Phillips and Karen J Goodman, (2006) Causal criteria and counterfactuals; nothing more (or less) than scientific common sense, Bio Med Central, 3:5, 1-7.

6. Carmen G. Loiselle, Joanne Profetto-McGrath, Denise F. Polit, (2010) Canadian Essentials of Nursing Research, 149..

Received on 11.02.2013 Modified on 20.03.2013

Asian J. Nur. Edu. and Research 3(2): April.-June 2013; Page 87-88