Computes Average Causal Effects by Covariate Adjustment in Binary Models using a Given Causal Model
Computes the average causal effect (ACE) of a given treatment variable X on a given
outcome Y for the models generated by
simulateWitnessModel. This assumes
the synthetic models are small enough, as adjustment is done by brute force calculation.
The algorithm implemented is a naive one. When creating the
cfx object, field
num_v_max must be
large enough so that the joint distribution is computed in advance. Only for relatively small models (approximately 20
variables in total) this will be feasible.
A list containing three different types of estimand:
the true ACE.
the result of a naive adjustment using all of the observed covariates.
the result of a naive adjustment using no covariates.
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## Generate a synthetic problem problem <- simulateWitnessModel(p = 4, q = 4, par_max = 3, M = 1000) ## Idealized case: suppose we know the true distribution, ## get "exact" ACE estimands for different adjustment sets sol_pop <- covsearch(problem, pop_solve = TRUE) effect_pop <- synthetizeCausalEffect(problem) cat(sprintf( "ACE (true) = %1.2f\nACE (adjusting for all) = %1.2f\nACE (adjusting for nothing) = %1.2f\n", effect_pop$effect_real, effect_pop$effect_naive, effect_pop$effect_naive2))
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