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Model Assumptions

All quantitative causal analyses involve two major groups of assumptions:

  1. Causal modeling assumptions
  2. Statistical modeling assumptions

Details about each of them can be found in specialized literature (Ferrari, forthcoming).

Although some checks for statistical modeling assumptions are provided, the causalinf module puts emphasis on the first type of assumptions, namely, the causal modeling assumptions. The main reasons is that the initial step in any causal inference analysis is selecting among various causal inference models. This selection largely hinges on assessments of the data generating process and which causal modeling assumptions are plausible in the specific context of application. Typically, users of causal inference methods know the causal assumptions associated with each causal modeling approach and then select the method whose assumptions are plausible in the available data. Hence, the emphasis of causalinf on those assumptions.

Users can find functionalities to test statistical modeling assumptions in various other existing modules outside causalinf. These external functionalities can be used directly on the output of the estimation object from causalinf (see Estimation). However, differently from many statistical modeling assumptions, the validity of causal modeling assumptions cannot be tested using typical statistical test procedures. Instead, the judgment about their validity depends on three things:

  1. Methodological expertise, which refers to the understanding of what the assumptions mean (and do not mean) in terms of the relations between the variables used and omitted in the model. The literature on causal inference methods provides guidance in this regard. Ferrari (forthcoming) is the reference text used for the causalinf implementation.
  2. Domain knowledge expertise, which refers to the understanding of how the data was generated and whether the assumptions plausibly hold for the particular intended application.
  3. Heuristic or suggestive evidence based on data. For some assumptions of specific methods, procedures exist to evaluate whether the causal modeling assumptions seem plausible given the data.

Whenever possible, the causalinf module facilitates that third aspect of the assessment of causal modeling assumptions. The main function in all submodules to get that assessment is check_assumptions(). For instance, to check the causal assumptions for a DiD application, use:

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from causalinf import did

did.check_assumptions(<args>)

<args> vary across submodules depending on the method used. See Methods for details in each case.

References

  • Ferrari, D. (forthcoming). The Identification of Causal Effects. Cambridge University Press.