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

Last update: Jan 03, 2023

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Overview

CausalNLP

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

Install

pip install -U pip
pip install causalnlp

Usage

Example: 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: 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: 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 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.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:  1.1179866790771484  sec
start fitting causal inference model
time to fit causal inference model:  10.361494302749634  sec

Estimating Treatment Effects

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.

Text is Optional in CausalNLP

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.

Documentation

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

How to Cite

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},
}

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Comments

Does your model support other languages than English?

Hi Amaiya, Thanks for your great package. Would you kindly let me know if your package supports languages other than English when using CausalBert?

I'm also interested in knowing whether I can exploit other Transformers models from the Huggingface hub?
question

opened by behroozazarkhalili 1

Error while fitting the model

Hi,

I ran to this bug while fitting the model. I checked the data and everything looks good. I don't get the root cause of this error.

File /opt/conda/lib/python3.8/site-packages/causalnlp/meta/slearner.py:80, in BaseSLearner.fit(self, X, treatment, y, p)
     78 mask = (treatment == group) | (treatment == self.control_name)
     79 treatment_filt = treatment[mask]
---> 80 X_filt = X[mask]
     81 y_filt = y[mask]
     83 w = (treatment_filt == group).astype(int)

IndexError: boolean index did not match indexed array along dimension 0

opened by hfarhidzadeh 1

Releases(v0.7.0)

v0.7.0(Aug 2, 2022)
0.7.0 (2022-08-02)

New:

N/A

Changed

updated dependencies

Fixed:

N/A

Source code(tar.gz)
Source code(zip)
v0.6.0(Oct 20, 2021)
0.6.0 (2021-10-20)

New:

Added model_name parameter to CausalBertModel to support other DistilBert models (e.g., multilingual)

Changed

N/A

Fixed:

N/A

Source code(tar.gz)
Source code(zip)
v0.5.0(Sep 3, 2021)
0.5.0 (2021-09-03)

New:

Added support for CausalBert

Changed

Added p parameter to CausalInferenceModel.fit and CausalInferenceModel.predict for user-supplied propensity scores in X-Learner and R-Learner.

Removed CV from propensity score computations in X-Learner and R-Learner and increase default max_iter to 10000

Fixed:

Resolved problem with CausalInferenceModel.tune_and_use_default_learner when outcome is continuous

Changed to max_iter=10000 for default LogisticRegression base learner

Source code(tar.gz)
Source code(zip)
v0.4.0(Sep 3, 2021)
0.4.0 (2021-07-20)

New:

N/A

Changed

Use LinearRegression and LogisticRegression as default base learners for s-learner.

changed parameter name of metalearner_type to method in CausalInferenceModel.

Fixed:

Resolved mis-references in _balance method (renamed from _minimize_bias).

Fixed convergence issues and factored out propensity score computations to CausalInferenceModel.compute_propensity_scores.

Source code(tar.gz)
Source code(zip)
v0.3.1(Jul 19, 2021)
0.3.1 (2021-07-19)

New:

N/A

Changed

N/A

Fixed:

Added sample_size parameter to CausalInferenceModel.evalute_robustness

Source code(tar.gz)
Source code(zip)
v0.3.0(Jul 15, 2021)
0.3.0 (2021-07-15)

New:

Added CausalInferenceModel.evaluate_robustness method to assess robustness of causal estimates using sensitivity analysis

Changed

reduced dependencies with local metalearner implementations

Fixed:

N/A

Source code(tar.gz)
Source code(zip)
v0.2.0(Jun 21, 2021)
0.2.0 (2021-06-21)

New:

key driver analysis

Changed

CausalInfererenceModel.fit returns self

Fixed:

N/A

Source code(tar.gz)
Source code(zip)
v0.1.3(Jun 17, 2021)
0.1.3 (2021-06-17)

New:

N/A

Changed

N/A

Fixed:

version fix

Source code(tar.gz)
Source code(zip)
v0.1.2(Jun 17, 2021)
0.1.2 (2021-06-17)

New:

N/A

Changed

Better interpretability and explainability of treatment effects

Fixed:

Fixes to some bugs in preprocessing

Source code(tar.gz)
Source code(zip)
v0.1.1(Jun 17, 2021)
0.1.1 (2021-06-16)

New:

N/A

Changed

Refactored DataFrame preprocessing

Fixed:

N/A

Source code(tar.gz)
Source code(zip)
v0.1.0(Jun 16, 2021)
0.1.0 (2021-06-15)

New:

First release.

Changed

N/A

Fixed:

N/A

Source code(tar.gz)
Source code(zip)

Owner

Arun S. Maiya

computer scientist

GitHub Repository https://amaiya.github.io/causalnlp/

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

Related tags

Overview

CausalNLP

Install

Usage

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

Estimating Treatment Effects

Text is Optional in CausalNLP

Documentation

How to Cite

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Comments

Does your model support other languages than English?

Error while fitting the model

Releases(v0.7.0)

v0.7.0(Aug 2, 2022)

0.7.0 (2022-08-02)

New:

Changed

Fixed:

v0.6.0(Oct 20, 2021)

0.6.0 (2021-10-20)

New:

Changed

Fixed:

v0.5.0(Sep 3, 2021)

0.5.0 (2021-09-03)

New:

Changed

Fixed:

v0.4.0(Sep 3, 2021)

0.4.0 (2021-07-20)

New:

Changed

Fixed:

v0.3.1(Jul 19, 2021)

0.3.1 (2021-07-19)

New:

Changed

Fixed:

v0.3.0(Jul 15, 2021)

0.3.0 (2021-07-15)

New:

Changed

Fixed:

v0.2.0(Jun 21, 2021)

0.2.0 (2021-06-21)

New:

Changed

Fixed:

v0.1.3(Jun 17, 2021)

0.1.3 (2021-06-17)

New:

Changed

Fixed:

v0.1.2(Jun 17, 2021)

0.1.2 (2021-06-17)

New:

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v0.1.1(Jun 17, 2021)

0.1.1 (2021-06-16)

New:

Changed

Fixed:

v0.1.0(Jun 16, 2021)

0.1.0 (2021-06-15)

New:

Changed

Fixed: