The Election Guide Digest has some interesting thoughts on the subject. Here is quoting part of the post:

The use of the RCT framework resolves two main problems that plague most D&G evaluations, namely the levels-of-analysis problem and the issue of missing baseline data. The levels-of-analysis problem arises when evaluations link programs aimed at meso-level institutions, such as the judiciary, with changes in macro-level indicators of democracy, governance, and corruption. Linking the efforts of a meso-level program to a macro-level outcome rests on the assumption that other factors did not cause the outcome.

An RCT design forces one to minimize such assumptions and isolate the effect of the program, versus the effect of other factors, on the outcome. By choosing a meso-level indicator, such as judicial corruption, to measure the outcome, the evaluator can limit the number of relevant intervening factors that might affect the outcome. In addition, because an RCT design compares both before/after in a treatment and control group, the collection of relevant baseline data, if it does not already exist, is a prerequisite for conducting the evaluation. Many D&G evaluations have relied on collecting only ex-post data, making a true before/after comparison impossible.

Yet it would be difficult to evaluate some “traditional” D&G programs through an RCT design. Consider an institution-building program aimed at reforming the Office of the Inspector General (the treatment group) in a country’s Ministry of Justice. If the purpose of the evaluation is to determine what effect the program had on reducing corruption in that office, there is no similar office (control group) from which to draw a comparison. The lack of a relevant control group and sufficient sample size is the main reason many evaluations cannot employ an RCT design.

More on this here.