Sensitivity Analysis

The function <submodule>.estimate(<args>) can be used in all submodules to run the estimation and collect inference statistics (p-value, z-scores, etc.). For instance, to estimate a DiD model, use:

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from causalinf import did

mod = did.estimate(<args>, data=<DataFrame>)

<args> vary across submodules depending on the method used.

Sensitivity analysis* is important because it provides information on how the conclusions of a causal inference study would change if one or more causal assumptions were violated in specific ways.

The core method across submodules of causalinf to run sensitivity analysis is sensitivity_analysis(<args>). For instance, if one is using Selection on Observables (SoO), sensitivity analysis can be conducted as follows:

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from causalinf import soo

sens = soo.sensitivity_analysis(<args>)

<args> vary across submodules depending on the method used.