Experimental and quasi-experimental designs play a central role in estimating cause-effect relationships in education, psychology, and many other fields of the social and behavioral sciences. This paper presents and discusses the causal graphs of experimental and quasi-experimental designs. For quasi-experimental designs the authors demonstrate that a causal treatment effect can be identified if they can isolate a subpopulation whose graph resembles the graph of an randomized controlled trial (RCT). For regression discontinuity (RD) designs, the corresponding subpopulation is given by the population in the very close neighborhood around the cutoff score; for the instrumental variable (IV) design, it is the subpopulation of compliers; for matching designs, it is the matched population and for propensity stratification (PS); for weighting designs it is the stratified or weighted population. The causal graphs also show that the identification of causal effects rests on stronger assumptions as the researchers' control over the study diminishes. More control usually implies the simpler data-generating mechanism and, thus, relative ease of identification. Figures are provided in the appendix. [SREE documents are structured abstracts of SREE conference symposium, panel, and paper or poster submissions.]