Question: Is it possible to extract inclusion probabilities of predictors from trained model?

[hnasko]

Hi, thank you all your efforts in developing this library

Previously, we used Google's initial R library, CausalImpact, for causal inference. Now, we're in search of a good substitute for it in Python, and this library seems like an excellent option. In the initial R library, there is functionality to extract posterior inclusion probabilities of predictors from the trained model. We use these probabilities as additional descriptive statistics to fine-tune the model and exclude certain predictors from the control group.

I'm curious if there's an approach to extract these posterior inclusion probabilities of predictors in tfcausalimpact. Your guidance on this matter would be immensely helpful.

Just R code to demonstrate what exactly I mean:

library(CausalImpact)

set.seed(1)
x1 <- 100 + arima.sim(model = list(ar = 0.88), n = 100)
x2 <- arima.sim(model = list(ar = 0.22), n = 100) + rnorm(100)
x3 <- rnorm(100) + rnorm(100, mean = 80, sd = 10)

y <- 1.2 * (x1+x2+x3)/3 + rnorm(100)

y[71:100] <- y[71:100] + 60
data <- cbind(y, x1, x2, x3)

pre.period <- c(1, 70)
post.period <- c(71, 100)

impact <- CausalImpact(data, pre.period, post.period)
plot(impact$model$bsts.model, "coefficients")

[WillianFuks]

Hi @hnasko ,

It is possible to extract the posterior of the weights. Please refer to the getting_started notebook section 2.5 where this is discussed:

If you used the default Hamiltonian method then this code should retrieve the posterior samples averaged:

tf.reduce_mean(ci.model.components_by_name['SparseLinearRegression/'].params_to_weights(
    ci.model_samples['SparseLinearRegression/_global_scale_variance'],
    ci.model_samples['SparseLinearRegression/_global_scale_noncentered'],
    ci.model_samples['SparseLinearRegression/_local_scale_variances'],
    ci.model_samples['SparseLinearRegression/_local_scales_noncentered'],
    ci.model_samples['SparseLinearRegression/_weights_noncentered'],
), axis=0)

Let me know if this solves for you.