Deep-Learning-for-Causal-Inference
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kochbj
Extensive tutorials for learning how to build deep learning models for causal inference (HTE) using selection on observables in Tensorflow 2.
In the [implementation](https://colab.research.google.com/github/kochbj/Deep-Learning-for-Causal-Inference/blob/main/Tutorial_2_Causal_Inference_Metrics_and_Hyperparameter_Optimization.ipynb#scrollTo=V1pELmlaISz0&line=11&uniqifier=1), `na` and `nb` is not used. ```python def pdist2sq(A, B): #helper for PEHEnn #calculates squared euclidean distance between rows of two matrices #https://gist.github.com/mbsariyildiz/34cdc26afb630e8cae079048eef91865 # squared norms of...
In [Tutorial 1](https://colab.research.google.com/drive/1Zx0AkriygB_ws6qXjA7VfqebG-YMwbWl#scrollTo=ixInwwcKmMfO), there is the following saying: > While you should experiment with different learning rates, I recommend having a conservative (smaller) learning rate because we really want our...
Hi, this is a great tutorial! Thank you for sharing. I have a question about implementing Dragonnet with a large dataset (in my case 200k subjects). Since to calculate loss...
Hi, Very usefull and great tutorials! Any plans to add a multi treatments tutorial with tarnet or dragonnet ? Thanks !
