It is one of the dimensions of parliamentary oversight. In this dimension, the parliament is engaged in a preventative evaluation of governmental conduct that precedes the formal adoption by the government of a decision, action or public policy. Parliaments scrutinise governmental proposals or draft documents to address future policy choices and exercise an influence over the sphere of action of the executive.

For further details, see: Parliamentary oversight

To estimate the effect of a policy, the individuals treated and those not treated should have the same initial characteristics, so that any changes in the result variable can be attributed solely to the public policy. When the initial characteristic are different, however, a selection bias occurs. We speak of bias because it prevents the use, to estimate the effect, of a comparison between the average result variable for those treated and the average for those not treated. The bias is caused by the selection because it depends on differences in the characteristics of treated and the non-treated individuals which existed before the public policy. These influence the allocation of individuals to the treated group or the non-treated group. These influence whether individuals are allocated to the treated group or the non-treated group.

Implementation analysis (see the relative entry) allows the study of the implementation of single interventions and articulated policies. The main methods used are based on qualitative techniques, which are now widely recognised for their reliability and employed in various fields of study. Their use is justified by their ability to produce reliable data even in complex contexts, both because of the nature and diversity of the actors involved and because of the characteristics of the policies themselves. Moreover, these techniques provide accurate information when systematic quantitative data collection or statistical analyses are not a viable option.

Specifically, these methods provide a detailed overview of the policy being analysed and identify mechanisms that may facilitate or hinder the successful implementation of a policy. Common methods include individual interviews, focus groups, participant observation, ethnography, narrative inquiry, action research and grounded theory. When designing an implementation analysis, it is essential to map the main actors and stakeholders affected by the intervention to identify the best method of data collection, always bearing in mind the theoretical reference framework, whether based on a top-down or bottom-up approach.

Therefore, the research team has to define in advance the actors to be interviewed, the frequency of meetings with them and the places where data will be collected. Whatever method is used, the interviews should be recorded and transcribed to provide clear guidelines for those carrying out these tasks and to provide the empirical basis for subsequent analysis. At the same time, other forms of documentation must be kept, such as field notes, i.e. notes taken during the data collection phase in order to enrich the information gathered.

Statistical matching is a method used to define a control group ex post byselecting, from the individuals excluded from the treatment (non-treated), those most similar (controls) to the individuals admitted to the treatment (treated), on the basis of observable characteristics. If the matching is carried out correctly, the effect of the treatment can be calculated simply as the difference between the average of the result variable for the treated individuals and the matched non-treated individuals. This represents an estimate of the counterfactual.

Statistical matching is said to use observations that have a common support, since it is conducted with individuals from the two most comparable groups on the basis of observable characteristics.

If there are a high number of characteristics to be considered, it can be particularly difficult to find the most similar individuals. To resolve this problem, a propensity score is generally used, i.e. the probability of an individual being treated, given his or her observable characteristics. It has been demonstrated that the essential condition for the elimination of selection bias on the basis of observable characteristics is met even if these characteristics are encapsulated by the propensity score alone.