CausalNLP is a practical toolkit for causal inference with text as treatment, outcome, or “controlled-for” variable.

• Low-code causal inference in as little as two commands
• Out-of-the-box support for using text as a “controlled-for” variable (e.g., confounder)
• Built-in Autocoder that transforms raw text into useful variables for causal analyses (e.g., topics, sentiment, emotion, etc.)
• Sensitivity analysis to assess robustness of causal estimates
• Quick and simple key driver analysis to yield clues on potential drivers of an outcome based on predictive power, correlations, etc.
• Can easily be applied to “traditional” tabular datasets without text (i.e., datasets with only numerical and categorical variables)
• Includes an experimental PyTorch implementation of CausalBert by Veitch, Sridar, and Blei (based on reference implementation by R. Pryzant)

1. pip install -U pip
2. pip install causalnlp

NOTE: On Python 3.6.x, if you get a RuntimeError: Python version >= 3.7 required, try ensuring NumPy is installed before CausalNLP (e.g., pip install numpy==1.18.5).

To try out the examples yourself:

import pandas as pd

df = pd.read_csv('sample_data/music_seed50.tsv', sep='\t', on_bad_lines='skip')

The file music_seed50.tsv is a semi-simulated dataset from here. Columns of relevance include: - Y_sim: outcome, where 1 means product was clicked and 0 means not. - text: raw text of review - rating: rating associated with review (1 through 5) - T_true: 0 means rating less than 3, 1 means rating of 5, where T_true affects the outcome Y_sim. - T_ac: an approximation of true review sentiment (T_true) created with Autocoder from raw review text - C_true:confounding categorical variable (1=audio CD, 0=other)

We’ll pretend the true sentiment (i.e., review rating and T_true) is hidden and only use T_ac as the treatment variable.

Using the text_col parameter, we include the raw review text as another “controlled-for” variable.

from causalnlp import CausalInferenceModel
from lightgbm import LGBMClassifier

cm = CausalInferenceModel(df, 
                         metalearner_type='t-learner', learner=LGBMClassifier(num_leaves=500),
                         treatment_col='T_ac', outcome_col='Y_sim', text_col='text',
                         include_cols=['C_true'])
cm.fit()

outcome column (categorical): Y_sim
treatment column: T_ac
numerical/categorical covariates: ['C_true']
text covariate: text
preprocess time:  1.1179866790771484  sec
start fitting causal inference model
time to fit causal inference model:  10.361494302749634  sec

CausalNLP supports estimation of heterogeneous treatment effects (i.e., how causal impacts vary across observations, which could be documents, emails, posts, individuals, or organizations).

We will first calculate the overall average treatment effect (or ATE), which shows that a positive review increases the probability of a click by 13 percentage points in this dataset.

Average Treatment Effect (or ATE):

print( cm.estimate_ate() )

{'ate': 0.1309311542209525}

Conditional Average Treatment Effect (or CATE): reviews that mention the word “toddler”:

print( cm.estimate_ate(df['text'].str.contains('toddler')) )

{'ate': 0.15559234254638685}

Individualized Treatment Effects (or ITE):

test_df = pd.DataFrame({'T_ac' : [1], 'C_true' : [1], 
                        'text' : ['I never bought this album, but I love his music and will soon!']})
effect = cm.predict(test_df)
print(effect)

[[0.80538201]]

Model Interpretability:

print( cm.interpret(plot=False)[1][:10] )

v_music    0.079042
v_cd       0.066838
v_album    0.055168
v_like     0.040784
v_love     0.040635
C_true     0.039949
v_just     0.035671
v_song     0.035362
v_great    0.029918
v_heard    0.028373
dtype: float64

Features with the v_ prefix are word features. C_true is the categorical variable indicating whether or not the product is a CD.

Despite the “NLP” in CausalNLP, the library can be used for causal inference on data without text (e.g., only numerical and categorical variables). See the examples for more info.

API documentation and additional usage examples are available at: https://amaiya.github.io/causalnlp/

Please cite the following paper when using CausalNLP in your work:

@article{maiya2021causalnlp,
    title={CausalNLP: A Practical Toolkit for Causal Inference with Text},
    author={Arun S. Maiya},
    year={2021},
    eprint={2106.08043},
    archivePrefix={arXiv},
    primaryClass={cs.CL},
    journal={arXiv preprint arXiv:2106.08043},
}