CausalNLP

CausalNLP is a practical toolkit for causal inference with text

Install

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

Usage

What is the causal impact of a positive review on a product click?

import pandas as pd
df = pd.read_csv('sample_data/music_seed50.tsv', sep='\t', error_bad_lines=False)

The file music_seed50.tsv is a semi-simulated dataset from here. Columns of relevance include:

• Y_sim: simulated outcome, where 1 means product was clicked and 0 means not.
• C_true:confounding categorical variable (1=audio CD, 0=other)
• T_true: 1 means rating less than 3, 0 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.

We'll pretend the rating and T_true are unobserved and only use T_ac as the treatment variable. Using the text_col parameter, we include raw text as covariates for which adjustments can be made to improve causal estimates.

from causalnlp.causalinference 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:  1.1216762065887451  sec
start fitting causal inference model
time to fit causal inference model:  9.701336860656738  sec

The average treatment effect (ATE):

print( cm.estimate_ate() )

{'ate': 0.1309311542209525}

The conditional average treatment effect (CATE) for those reviews that mention the word "toddler":

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

{'ate': 0.15559234254638685}

Features most predictive of the treatment effects (e.g., increase in probability of clicking product):

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.