As educational research increasingly emphasizes identifying effective interventions through rigorous causal methods, the role of implementation in determining a program's impact has gained renewed significance. Despite the long-standing recognition that implementation varies across contexts and influences outcomes (Berman & McLaughlin, 1974; Durlak & DuPre, 2008; McLaughlin, 1990), the field continues to grapple with the best ways to conceptualize, measure, and integrate implementation into quantitative studies. This paper presents a systematic review of 79 intervention studies funded by the Institute of Education Sciences (IES) between 2007 and 2020 to examine how implementation is defined, operationalized, and analyzed in contemporary educational research. It explores the persistent tensions between fidelity and adaptation as frameworks for conceptualizing implementation. Additionally, it analyzes the conceptual and methodological implications for causal inference, particularly how implementation data is condensed and the methods used to account for this data when estimating intervention effects. Findings reveal that while 85% of studies report measuring implementation, the overwhelming majority employ a fidelity framework--primarily defined as adherence to the program's intended design (O'Donnell, 2008). Constructs such as adherence (measured in 79% of studies), dosage (33%), and quality (35%) dominate implementation measurement, while others like program differentiation and participant responsiveness, are rarely considered. In the specific case of quality, some studies conflate general instructional quality with program-specific implementation, raising questions about whether what is being measured reflects fidelity to the intervention or general pedagogical practices. Despite the diversity of programs evaluated, implementation is often treated as a monolithic construct, typically reduced to single scores using simple averages or summative indexes. Only a minority of studies employ latent variable models, such as confirmatory factor analysis or latent profile analysis, to explore underlying structures in fidelity data (e.g., Stylianou et al., 2019; Sullivan et al., 2016). The instruments used to collect implementation data are primarily ad hoc tools developed by research teams for specific interventions. Classroom observations are the most common measurement approach (82% of studies that measure fidelity), followed by teacher logs (25%) and surveys (25%). Benchmarking of implementation fidelity is also inconsistent. Some studies report preestablished theoretical thresholds for defining acceptable implementation. For example, they may use benchmarks such as 75% or 80% compliance to determine acceptable levels of fidelity. Others use the distribution of fidelity scores (means or quartiles, or the percentage of activities completed relative to the total agreed upon), generating ex-post classifications. Another group of studies reported achieving "high," "good," "moderate," or "acceptable" levels of implementation without indicating the specific expected levels or thresholds. While some studies define thresholds for "adequate" fidelity (e.g., 75% adherence), many report achieving high implementation without describing what instructional practices implemented with fidelity look like. Although some studies suggest specific benchmarks, they often do not justify why these levels are deemed sufficient or how they relate to the different implementation measures. This obscures the relationship between program theory (the hypothesized effect of the intervention on student outcomes) and the enactment of the intervention (the instructional practices the students were exposed to). This lack of clarity complicates efforts to interpret whether the intervention was implemented as intended and raises questions about the internal validity of impact estimates (Century & Cassata, 2016; Rossi et al., 2019). Moreover, only 23 of the 66 studies that measure implementation include data on fidelity in their analyses of student outcomes. The findings from these studies are mixed: some show positive correlations between fidelity and achievement (Duncan Seraphin et al., 2017), others find null or inconsistent effects (Apthorp et al., 2012; Rimm-Kaufman et al., 2014), and a few observe negative associations, suggesting that high fidelity does not always lead to better outcomes (Gómez et al., 2023). Adaptation, in contrast, is seldom conceptualized or measured systematically. Only nine of the reviewed studies include any analysis of teacher modifications to the intervention. Those that do typically employ qualitative methods such as interviews or open-ended surveys and often focus on small samples. Nevertheless, these studies indicate that adaptations are not random deviations but rather intentional modifications aimed at better aligning instruction with students' needs (Burkhauser & Lesaux, 2017; Monte-Sano et al., 2014). In some cases, teachers' pedagogical content knowledge and awareness of their students' learning trajectories drive changes that may enhance the program's impact (McKeown et al., 2023). Yet, in the broader body of research, adaptation is often implicitly treated as a threat to internal validity, and teacher agency is marginalized within implementation measurement frameworks. This review underscores the conceptual limitations and methodological inconsistencies in how implementation is addressed in quantitative education research. The emphasis on fidelity--particularly adherence--reflects the priorities of federal funders and the methodological demands of experimental and quasi-experimental designs. However, this approach often neglects the productive. The findings suggest that the prevailing focus on adherence may obscure the mechanisms through which interventions affect learning and lead to under-theorized and incomplete explanations of program effectiveness. Moving forward, research designs must better account for the dynamic nature of implementation. This includes: (1) Differentiating between overall instructional quality and program-specific fidelity; (2) Using theory-driven logic models to identify core components and specify expectations for their enactment (Weiss, 1995; W.K. Kellogg Foundation, 2004); (3) Incorporating multiple sources and methods for measuring implementation, including triangulation across quantitative and qualitative data sources (Lesaux et al., 2014); and (4) Consider adaptation not as a binary opposite of fidelity, but as a construct deserving independent measurement and theoretical attention (Century & Cassata, 2016; Cho, 1998). Recognizing that fidelity and adaptation coexist in real-world implementation opens the door to a more nuanced understanding of how educational interventions function within their context. Rather than viewing teacher deviations from program design as methodological noise, researchers can regard them as a source of insight into instructional improvement. By developing robust and flexible frameworks for measuring implementation, the field can progress toward generating not only evidence of what works but also a deeper knowledge of how, for whom, and under what conditions interventions can be most effective.