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Many causal queries, especially conditional average treatment effects (CATE), can be understood as the application of an intervention to a posterior predictive distribution of a causal Bayesian networ…
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Hi everyone,
I am trying to run a causal forest on an experiment with 10 different treatments.
If i were running 9 separate causal forest, i would implement feature selection on every forest by run…
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The below post is more of a methodological question than a technical one.
Based on what I've gathered, the _honest causal forest_ and _policy tree_ are two distinct yet related methods. Both can ev…
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For example, we want to do a type III ANOVA, so we fit a linear model with categorical predictors and use the car::Anova function:
```r
some_linear_model
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Hello again,
I have been trying to implement DML methods to predict the causal effect of a continuous treatment (from 0 to 300 aprox) and even if I got to the point of having a fairly good MSE scor…
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Is there a method for predicting optimal treatments based on an unseen dataset of covariates? Is there a valid way to choose an optimal treatment for an individual record?
If it makes a difference,…
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I got a scenario that have categorical but non-binary treatment (can up to five option). Does DML and its variances, or metalearner support such scenario? It seems DML assumes partial treatment effect…
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Hi there,
maybe the question is trivial but I couldn't really figure it out by reading the paper and README of the repo: How would I specify `numPrePeriods` and `numPostPeriods` in the call to `cre…
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https://github.com/BIDData/BIDMach/wiki/Causal-Inference
it is how it is shown
IPTW
BIDMach has several basic causal estimators. IPTW stands for Inverse Probability of Treatment Weighting and is…
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Problem:
I think there should be some ways to identify subgroups suffer heterogeneous treatment effects. e.g **tree** structure can help us to tell that. It seems that SHAP targets only on 1-order …