However, when researchers are attempting to explain causal effects, there are certain criteria that have to be met.
1. An experimental design is the only method by which a causal effect can be inferred. This means that study participants are selected at random and randomly assigned to treatment groups. Any differences at baseline are thought to occur by chance with random selection and random assignment (whereas with observational designs they are due to selection and observation biases).
2. Analyses have to be conducted in an "intention-to-treat" fashion. This means that all study participants are analyzed in the original groups that they were randomly assigned to at the beginning of the study.
3. Blinding can bolster the validity of causal effects, but is not necessary for establishing causal effects. Blinding can reduce observational biases that can occur in experiments where human beings are studied.
4. As much as possible, all pertinent demographic, clinical, and confounding variables related to the association between the primary predictor and primary outcome variable should be accounted for in experiments. True causal effects (in reality and practice) are always multivariate in nature. Clinicians do not make treatment decisions based on one form of evidence, they want as many forms of evidence as possible. The statistics that establish the efficacy of treatments should reflect the multivariate nature of treatment decisions.
5. There has to be a sufficient amount of time or follow-up for an outcome. True causal effects are found when the temporal aspects (etiological, prognostic, time) of developing an outcome are accounted for methodologically and statistically.
6. One clinical trial that shows valid evidence of a causal effect and meets all of the aforementioned criteria is STILL NOT ENOUGH! The results of the most methodologically sound randomized controlled trials or true experiments need to be aggregated in systematic reviews, syntheses, and synopses of syntheses. Pooled effects account for more variance in the general population and strengthen the "causal" understanding of causal effects.