{'fraction_non_alphanumeric': 0.026386645126548196, 'fraction_numerical': 0.007539041464728056, 'mean_word_length': 5.483420593368237, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Process Tracing Tests for Causal Inference

Process tracing is a fundamental tool of qualitative analysis. This method is often invoked by scholars who carry out within-case analysis based on qualitative data, yet frequently it is neither adequately understood nor rigorously applied. This deficit motivates this article, which offers a new framework for carrying out process tracing. The reformulation integrates discussions of process tracing and causal-process observations, gives greater attention to description as a key contribution, and emphasizes the causal sequence in which process-tracing observations can be situated. In the current period of major innovation in quantitative tools for causal inference, this reformulation is part of a wider, parallel effort to achieve greater systematization of qualitative methods. A key point here is that these methods can add inferential leverage that is often lacking in quantitative analysis. This article is accompanied by online teaching exercises, focused on four examples from American politics, two from comparative politics, three from international relations, and one from public health/epidemiology. Process tracing is a fundamental tool of qualitative analysis. In the framework presented here,1 it is defined as the systematic examination of diagnostic evidence selected and analyzed in light of research questions and hypotheses posed by the investigator. Process tracing can contribute decisively both to describing political and social phenomena and to evaluating causal claims. George and Bennett have played the leading role in developing this method as an essential form of within-case analysis,2 and Fenno’s “soaking and poking” is a kindred research procedure.3 Although the idea of process tracing is often invoked by scholars as they examine qualitative data, too often this tool is neither well understood nor rigorously applied. Relatedly, the field of qualitative methods in political science—in sharp contrast to quantitative methods—is inadequately equipped with procedures for teaching basic research tools, including process tracing. This two-fold deficit motivates this article, which offers a new framework for understanding, applying, and teaching process tracing. The approach is distinctive in three ways. Process Tracing vis-à-vis CPOs. The evidence on which process tracing focuses corresponds to what Collier, Brady, and Seawright (2010a) call causal-process observations, or CPOs. The idea of CPOs highlights the contrast between (a) the empirical foundation of qualitative research, and (b) the data matrices analyzed by quantitative researchers, which may be called data-set observations (DSOs). Some of the literature on which this article draws (e.g., Brady 2010; Freedman 2010a; Mahoney 2010) formulates arguments in terms of CPOs, rather than in terms of process tracing per se. The present article treats these methodological tools as two facets of the same research procedure. Throughout, the article consistently refers to “process tracing” to avoid applying two labels to what is basically the same method. Description. Careful description is a foundation of process tracing, a perspective emphasized by Mahoney (2010, 125–31). Process tracing inherently analyzes trajectories of change and causation, but the analysis fails if the phenomena observed at each step in this trajectory are not adequately described. Hence, what in a sense is “static” description is a crucial building block in analyzing the processes being studied. Sequence. Process tracing gives close attention to sequences of independent, dependent, and intervening variables. Again, we follow Mahoney, who has productively advanced this approach.