[[Judea Pearl]]<ref name="Pearl09" /> extended SEM from linear to nonparametric models, and proposed causal and counterfactual interpretations of the equations. Nonparametric SEMs permit estimating total, direct and indirect effects without making any commitment to linearity of effects or assumptions about the distributions of the error terms.<ref name="BP13" />
SEM analyses are popular in the social sciences because computerthese programsanalytic maketechniques ithelp possibleus tobreak estimatedown complicatedcomplex concepts and understand causal structuresprocesses, but the complexity of the models introducescan introduce substantial variability in the results, with or without experimental design, depending on the usepresence or absence of conventional statisticalcontrol controlvariables, the consequences of sample size, and the variables of interestsinterest.<ref>{{Citation |last=Bollen |first=Kenneth A. |title=Eight Myths About Causality and Structural Equation Models |date=2013 |work=Handbooks of Sociology and Social Research |pages=301–328 |url=https://doi.org/10.1007/978-94-007-6094-3_15 |access-date=2024-12-11 |place=Dordrecht |publisher=Springer Netherlands |isbn=978-94-007-6093-6 |last2=Pearl |first2=Judea}}</ref> The use of experimental designs may address some of these doubts.<ref>{{Cite journal |last=Ng |first=Ted Kheng Siang |last2=Gan |first2=Daniel R.Y. |last3=Mahendran |first3=Rathi |last4=Kua |first4=Ee Heok |last5=Ho |first5=Roger C-M |date=2021-09 |title=Social connectedness as a mediator for horticultural therapy's biological effect on community-dwelling older adults: Secondary analyses of a randomized controlled trial |url=https://doi.org/10.1016/j.socscimed.2021.114191 |journal=Social Science & Medicine |volume=284 |pages=114191 |doi=10.1016/j.socscimed.2021.114191 |issn=0277-9536}}</ref>
