Chrisejorge/causal-text-papers

Curated research at the intersection of causal inference and natural language processing.

Papers about Causal Inference and Language

A collection of papers and codebases about influence, causality, and language.

Pull requests welcome!

Datasets and Simulations

Type	Description	Code
Semi-simulated	Given text (amazon reviews), extracts treatments (0 or 5 stars) and confounds (product type), then samples outcomes (sales) conditioned on the extracted treatments and confounds.	git
Fully synthetic	Samples outcomes, treatments, and confounds from binomial distributions, then words from a uniform distribution conditioned on those sampled variables.	git

Learning resources and blog posts

Title	Description	Code
Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates Katherine A. Keith, David Jensen, and Brendan O’Connor	Survey of studies that use text to remove confouding. Also highlights numerous open problems in the space of text and causal inference.
Text Feature Selection for Causal Inference Reid Pryzant and Dan Jurafsky	Blog post about text as treatment (operationalized through lexicons)	git

Causal Inference with Text Variables

Text as treatment

Title	Description	Code
Causal Effects of Linguistic Properties Reid Pryzant, Dallas Card, Dan Jurafsky, Victor Veitch, Dhanya Sridhar	Develops an adjustment procedure for text-based causal inference with classifier-based treatments. Proves bounds on the bias	git
Challenges of Using Text Classifiers for Causal Inference Zach Wood-Doughty, Ilya Shpitser, Mark Dredze	Looks at different errors that can stem from estimating treatment labels with classifiers, proposes adjustments to account for said errors	git
Deconfounded Lexicon Induction for Interpretable Social Science Reid Pryzant, Kelly Shen, Dan Jurafsky, Stefan Wager	Looks at effect of text as manifested in lexicons or individual words, proposes algorithms for estimating effects and evaluating lexicons	git
How to Make Causal Inferences Using Texts Naoki Egami, Christian J. Fong, Justin Grimmer, Margaret E. Roberts, and Brandon M. Stewart	(Also text as outcome). Covers assumptions needed for text as treatment and concludes that you should use a train/test set.
Discovery of treatments from text corpora Christian Fong, Justin Grimmer	Propose a new experimental design and statistical model to simultaneously discover treatments in a corpora and estimate causal effects for these discovered treatments.
The effect of wording on message propagation: Topic and author-controlled natural experiments on twitter Chenhao Tan, Lillian Lee, and Bo Pang	Controls for confouding by looking at Tweets containing the same url and written by the same user but employing different wording.

Text as mediator

Title	Description	Code
Adapting Text Embeddings for Causal Inference Victor Veitch, Dhanya Sridhar, and David Blei	(also text as confounder) Adapts BERT embeddings for causal inference by predicting propensity scores and potential outcomes alongside masked language modeling objective.	tensorflow pytorch

Text as outcome

Title	Description	Code
Estimating Causal Effects of Tone in Online Debates Dhanya Sridhar and Lise Getoor	(Also text as confounder). Looks at effect of reply tone on the sentiment of subsiquent responses in online debates.	git
How Judicial Identity Changes the Text of Legal Rulings Michael Gill and Andrew Hall	Looks at how the random assignment of a female judge or a non-white judge affects the language of legal rulings.
Measuring semantic similarity of clinical trial outcomes using deep pre-trained language representations Anna Koroleva, Sanjay Kamath, Patrick Paroubek

Text as confounder

Title	Description	Code
Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates Katherine A. Keith, David Jensen, and Brendan O’Connor	Survey of studies that use text to remove confouding. Also highlights numerous open problems in the space of text and causal inference.
Adjusting for confounding with text matching Margaret E Roberts, Brandon M Stewart, and Richard A Nielsen	Estimate a low-dimensional summary of the text and condition on this summary via matching to remove confouding. Proposes a method of text matching, topical inverse regression matching, that matches on both on the topical content and propensity score.
Matching with text data: An experimental evaluation of methods for matching documents and of measuring match quality Reagan Mozer, Luke Miratrix, Aaron Russell Kaufman, L Jason Anastasopoulos	Characterizes and empirically evaluates a framework for matching text documents that decomposes existing methods into: the choice of text representation, and the choice of distance metric.
Learning representations for counterfactual inference Fredrik Johansson, Uri Shalit, David Sontag	One of their semi-synthetic experiments has news content as a confounder.

Causality to Improve NLP

Causal interpretations and explanations

Title	Description	Code
CausaLM: Causal Model Explanation Through Counterfactual Language Models Amir Feder, Nadav Oved, Uri Shalit and Roi Reichart	Suggested a method for generating causal explanations through counterfactual language representations.	git
Causal Mediation Analysis for Interpreting Neural NLP: The Case of Gender Bias Jesse Vig, Sebastian Gehrmann, Yonatan Belinkov, Sharon Qian, Daniel Nevo, Yaron Singer and Stuart Shieber	Uses causal mediation analysis to interpret NLP models.	git

Sensitivity and Robustness

Title	Description	Code

Applications in the Social Sciences

Linguistics

Title	Description	Code
Decoupling entrainment from consistency using deep neural networks Andreas Weise, Rivka Levitan	Isolated the individual style of a speaker when modeling entrainment in speech.
Estimating causal effects of exercise from mood logging data Dhanya Sridhar, Aaron Springer, Victoria Hollis, Steve Whittaker, Lise Getoor	Confouder: Text of mood triggers. Confounding adjustment method: Propensity score matching

Marketing

Title	Description	Code
Predicting Sales from the Language of Product Descriptions Reid Pryzant, Young-Joo Chung, and Dan Jurafsky	Found features of product descriptions most predictive of sales while controlling for brand & price.	git
Interpretable Neural Architectures for Attributing an Ad’s Performance to its Writing Style Reid Pryzant, Kazoo Sone, and Sugato Basu	Found features of ad copy most predictive of high CTR while controlling for advertiser and targeting.	git

Persuasion & Argumentation

Title	Description	Code
Influence via Ethos: On the Persuasive Power of Reputation in Deliberation Online Emaad Manzoor, George H. Chen, Dokyun Lee, Michael D. Smith	Controls for unstructured argument text using neural models of language in the double machine-learning framework.

Mental Health

Title	Description	Code
The language of social support in social media and its effect on suicidal ideation risk Munmun De Choudhury and Emre Kiciman	Confouder: previous text written in a Reddit forum. Confounding adjustment method: stratified propensity scores matching.
Discovering shifts to suicidal ideation from mental health content in social media Munmun De Choudhury, Emre Kiciman, Mark Dredze, Glen Coppersmith, Mrinal Kumar	Confouder: User’s previous posts and comments received. Confounding adjustment method: stratified propensity scores matching

Psychology

Title	Description	Code
Increasing vegetable intake by emphasizing tasty and enjoyable attributes: A randomized controlled multisite intervention for taste-focused labeling Bradley Turnwald, Jaclyn Bertoldo, Margaret Perry, Peggy Policastro, Maureen Timmons, Christopher Bosso, Priscilla Connors, Robert Valgenti, Lindsey Pine, Ghislaine Challamel, Christopher Gardner, Alia Crum	Did RCT on cafeteria food labels, observing effect on how much of those foods students took.
A social media study on the effects of psychiatric medication use Koustuv Saha, Benjamin Sugar, John Torous, Bruno Abrahao, Emre Kıcıman, Munmun De Choudhury	Confounder: users' previous posts on Twitter. Confounding adjustment method: Stratified propensity score matching.

Economics

Title	Description	Code
A deep causal inference approach to measuring the effects of forming group loans in online non-profit microfinance platform Thai T Pham and Yuanyuan Shen	Confounder: Microloan descriptions on Kiva. Confounding adjustment method: A-IPTW, TMLE on embeddings.

Bias and Fairness

Title	Description	Code
Unsupervised Discovery of Implicit Gender Bias	Propensity score matching and adversarial learning to get a model to focus on bias instead of other artifacts.
Tweetment Effects on the Tweeted: Experimentally Reducing Racist Harassment Kevin Munger	Did RCT sending de-escalation messages to racist twitter users, changing the "from" user and observing effects on downstream behavior.

Social Media

Title	Description	Code
Estimating the effect of exercising on users online behavior Seyed Amin Mirlohi Falavarjani, Hawre Hosseini, Zeinab Noorian, Ebrahim Bagheri	Confouder: Pre-treatment topical interest shift. Confounding adjustment method: Matching on topic models.
Distilling the outcomes of personal experiences: A propensity-scored analysis of social media Alexandra Olteanu, Onur Varol, Emre Kiciman	Confouder: Past word use on Twitter. Confoudnig adjustment method: Stratified propensity score matching.
Using longitudinal social media analysis to understand the effects of early college alcohol use Emre Kiciman, Scott Counts, Melissa Gasser	Confounder: Previous posts on Twitter. Confounding adjustment method: Stratified propensity score matching.