Questions tagged [bart]

BART is a non-parametric Bayesian regression approach which uses dimensionally adaptive random basis elements.

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Poor fitted vs. actual values

I'm using a BART model (Bayesian additive regression tree) to predict the relative risk of an outcome (21,384 observations) controlling for 388 features and I'm getting a really poor actual vs. fitted ...
Tim's user avatar
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BART with non-parametric heteroscedastic noise?

Is there a variant of BART that robustly captures noise that is both heteroscedastic and non-parametric (or has an a-priori unknown parametric form)? For example, a BART that could fit this test data: ...
Luke Gorrie's user avatar
2 votes
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Bayesian Additive Regression Trees: Zero-inflated explanatory variable, will it influence the model and variable selection?

I am currently implementing BART to model the distribution of a marine species (using the embarcadero package). I am using environmental covariates, but also some prey data that are very-much zero-...
Timelate's user avatar
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3 votes
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Using a Bayesian Additive Regression Trees model for causal inference

Some Context: I've read this presentation about using a BART model to find out the causal effect of a certain variable with respect to a target variable (say, how much does a specific medicine ...
eduardokapp's user avatar
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Can we use Bootstrap to estimate MSE?

The classic problem in causality is that we do not observe the ground truth--the actual treatment effect. Let's say I have several methods in my toolkit (CART, causal tree, causal forest, BART etc.) ...
Divya Singh's user avatar
1 vote
1 answer
186 views

Metropolis Hastings for BART: Calculation of Tree Prior and Transition Kernel

I am trying to understand the details of BART (Bayesian Additive Regression Trees). In particular, I would like to know how the Metropolis Hastings acceptance probability is calculated for BART. My ...
AsgerTheDuck's user avatar
8 votes
2 answers
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Heterogeneous Treatment Effects with Continuous Treatment (e.g. using BART)

Overview: Most of the causal inference literature (both theoretical and applied), I have seen on heterogeneous treatment effects, only considers the case with a binary treatment $T\in\{0,1\}$. However,...
AsgerTheDuck's user avatar
11 votes
2 answers
729 views

Propensity score matching vs non-parametric regression

I am trying to understand the benefit of propensity matching over non-parametric regression for causal inference from non-experimental data. As background: the way I understand it, parametric ...
Shade's user avatar
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Why is BART so good?

It seems that BART (bayesian additive regresssion trees) is a very widely and successfully used method for causal inference. However this method was designed as a predictive method and does not ...
Jack Blackwell's user avatar
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Bimodal posterior of ATE predicted via Bayesias Additive Regression Trees

I am using BART to estimate the ATE on a large (10k obs x 224 p) dataset with a binomial outcome. In short, I first model the risk of the outcome $Y$ given the $(Z,X)$ covariates, then, for a chosen ...
Bakaburg's user avatar
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2 votes
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Average treatment effect from matrix of individual posterior distributions

I'm trying to estimate the average treatment effect of an intervention using the potential outcomes framework in a classification problem. The analysis uses machine learning to learn $\hat{y} = f(Y, X,...
Bakaburg's user avatar
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How come the BART results are this good at the 2016 Atlantic causal inference competition?

The famous paper Dorie,2017 shows that BART performs dramatically well in causal inference. In my replication, MSE in BART can be 40% lower than MSE in other machine learning methods. But all machine ...
Ruiyuan Huang's user avatar
3 votes
1 answer
54 views

Distribution of Decison Tree(s)

In a Random Forest each tree is identical distributed (i.d) with a mean E and variance S. I then wonder: what is the ...
CutePoison's user avatar
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3 answers
535 views

Heterogeneous Treatment Effects - Interpretable Methods?

I have data from an experiment I ran in which I paired individuals up to play a game with another person. Before and after the game, some baseline and endline measures are collected, and the DV of ...
Parseltongue's user avatar
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5 votes
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How does BART (Bayesian Additive regression tree) help with causal inference?

I have recently learned about using BART for causal inference from observational studies. So, I read that if we want to see the causal effect of a variable Z (binary) on Y in presence of X covariates ...
Roopali Singh's user avatar
3 votes
1 answer
538 views

How to compare different causal inference methodologies for estimating Average Treatment Effect when true treatment effect is unknown?

I'm comparing various methods for estimating average treatment effects (ATEs) for cost savings in a case-control study on health insurance episode of care data for my employer. My company currently ...
RobertF's user avatar
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6 votes
2 answers
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Favored methods for overcoming selection bias (special attention to healthcare fields)?

I am frequently measuring the effect of behavioral health treatment interventions on outcomes of interest. However, comparing the relative efficacy of different types of treatment is tricky - more ...
ShannonC's user avatar
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4 votes
1 answer
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Using Bayesian Lasso with an informed prior

I'm looking for advice on how best to go about setting an informative prior for the Bayesian Lasso and BART (I'm applying these in R using the rjags and bartMachine packages) I have 3 proteomics ...
David Cox's user avatar
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1 answer
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Unable to predict using bart() {BayesTree}

I used bart function from BayesTree library to build a model on my training data. It fits my training data very well. However, I'm unable to predict for the test set and check its performance. ...
Naveen Mathew's user avatar
1 vote
0 answers
286 views

Covariance for a multivariate Bayesian Additive Regression Tree

Chipman, George, and McCullogh (2010) state that: One can also extend the sum-of-trees model to a multivariate framework such as: $$ (29) \qquad\qquad Y_i = h_i\left( x_i \right) + \varepsilon_i, ...
shadowtalker's user avatar
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12 votes
2 answers
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MCMC sampling of decision tree space vs. random forest

A random forest is a collection of decision trees formed by randomly selecting only certain features to build each tree with (and sometimes bagging the training data). Apparently they learn and ...
highBandWidth's user avatar
4 votes
1 answer
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Bayesian additive regression trees (BART) for classification analysis of gene expression data

I am interested in applying Bayesian additive regression trees (BART) for classification analysis of gene expression data. I am relatively new to R (and Bioconductor packages) and I am unable to find ...
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