cannot allocate memory with CausalAnalysis().fit()

OS Architecture:

Operating System: Ubuntu 20.04.4 LTS
Kernel: Linux 5.15.0-53-generic
Architecture: x86-64

With a DataFrame of size `(rows, columns) = (9000, 102)`, the `fit` method I often get the cannot allocate memory

from econml.solutions.causal_analysis import CausalAnalysis
import warnings
warnings.simplefilter(action='ignore')
ca = CausalAnalysis(feature_inds=top_features, categorical=categorical,
heterogeneity_inds=None,
classification=True,
nuisance_models="automl",
heterogeneity_model="forest",
n_jobs=-1,
random_state=1234)

ca.fit(X, y)

As a temporary solutuion and some research I have managed a bandaid solution with following linux commands but did not solve the issue

swapon --show
sudo swapoff /swapfile
sudo fallocate -l 5G /swapfile
ls -lh /swapfile

Initially I had `1G `allocated. With the above syntax, now the swap file has `5GB`  which did not solve the issue

My issue is if the fit function fails with small dataset with 9000 rows while plenty of the unallocated memory available (I have ram with 64 GB, unused memory about 20GB), has there been similar issue reported? 

Some references:
https://stackoverflow.com/questions/5306075/python-memory-allocation-error-using-subprocess-popen
https://stackoverflow.com/questions/20111242/how-to-avoid-errno-12-cannot-allocate-memory-errors-caused-by-using-subprocess
https://stackoverflow.com/questions/20111242/how-to-avoid-errno-12-cannot-allocate-memory-errors-caused-by-using-subprocess

The reported entry point is CausalAnalysis().fit() with nuisance_models="automl", heterogeneity_model="forest", and n_jobs=-1 on a (9000, 102) DataFrame under Ubuntu 20.04. Reproduce the memory-allocation failure with the supplied configuration; done means identifying a confirmed cause and documenting or testing a corrective change.