Evaluation has a long history of using experimental and quasi-experimental designs to measure the effects of programs and strategies, and through this, to infer causality. Yet, these approaches are often not appropriate when evaluating change in complex, dynamic systems. Further, programs and strategies that seek to produce change in complex settings are increasingly common. This leads to an evaluation dilemma: if and how do evaluators attend to causality amid complexity? Too often, the answer has been to use causal thinking in the design of the evaluation and interpretation of findings, but not incorporate causal analysis and inference. In practice, this allows for assumptions about how change happens to go unchallenged. In this chapter, we explore the use of causal analysis through methods designed to attend to complexity and context, strengthened by participatory implementation, that can be implemented with rigor.

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