Transformers are unary functions that are applied on the covariates. Here is an example of “to the power of 5”.
Which you can pass it into the transformers list as
gen_pseudo_pop function tries transformers if the
covariate balance test has not been met in the previous attempt. The
covariate with the worst balance value will be chosen to apply a
transformer. The first transformer from the list will be selected for
this purpose. If the transformer has been used for this specific
covariate, the next value will be selected.
You can use
set_logger function and set logger_level to
one of “TRACE”, “DEBUG”, “INFO”, “SUCCESS”, “WARN”, “ERROR”, or”FATAL”.
In this package most of the internal information are logged in INFO and
DEBUG level. If you need to see a new information in the .log file,
please consider opening and issue here.
We are using a spawning mechanism in multicore processing. Each worker processor gets a copy of the required data and libraries. In case of limited available memory and a large dataset, you can reduce the number of CPU cores (nthread) to fit the processing into your system. Following this recommendation, the processing time will increase; however, the memory usage will decrease.
Many internal libraries (e.g.,
XGBoost) are dependent on
OpenMP library for parallel computation. Please make sure that you have
installed OpenMP library and configured it correctly. Please see the
following links for more details:
In order to activate OpenMP on HPC, you need to load the required modules. For example, if you are using SLURM on Cannon at Harvard University, you need to load the intel module.
Please read more here.
counter_weightcolumn in the pseudo population?
Both matching and weighting approaches find a combination of the
original data set to pass the covariate balance test. In the case of
matching, the package uses a different number of data samples. Some data
samples are never used; hence their
counter_weight value is
0. These data samples are probably far from the common support area, or
the resolution for
w is not fine enough. In the case of
weighting, this is the inverse probability of getting exposure. In the
old versions (before
ver0.2.9), this column was counter in
the matching approach and
ipw in the weighting approach,
One can either generate synthetic data using
generate_syn_data() function, or use
synthetic_us_2010 data that comes with the package. Other
datasets are mostly L3 data and cannot be shared with the public.
This question is commonly asked by researchers coming from matching with binary exposures. In the CausalGPS package (and the algorithm), for each exposure level and for each data sample, we generate a new data sample that poses the requested exposure level; however, it has a different GPS value. We find the closest original data (in terms of w and GPS) to the generated pseudo-data sample. So data are matched for the requested exposure level, not based on each original data sample. Therefore in the context of the CausalGPS package, it is not a correct question.
The most updated version is under
branch and the latest release is under
generate_pseudo_pop() function trims the entire data
based on the trimming quantiles. All other processes (e.g., estimating
gps, compiling pseudo population, matching, weighting, …) use the
trimmed data. Trimming data is an open research question, and many
different configurations can be considered.
Yes. But any rows with missing values will be eliminated from the process.
scale = 1is faster than any other amount. Is that correct?
That is correct. When the scale is 1, we compute the distance only based on GPS values. In this case, the algorithm will be simplified to a special case with the average time complexity of \(O(n.log(n))\) instead of \(O(n^2)\). Please note that we still use the subset of data within the caliper boundary for matching purposes. As a result, the exposure level for matched data is in a valid range.
Error in checkForRemoteErrors(val) : one node produced an error: length(xeval) < .maxEvalPts is not TRUE.
This issue arises due to a constraint within the
package. A feasible solution to circumnavigate this problem is to
install a modified version of locpol.
The deviation from the original package lies in the augmentation of the maximum number of evaluation points (.maxEvalPts).