diff --git a/README.md b/README.md index 9e6f877..79018ee 100644 --- a/README.md +++ b/README.md @@ -18,7 +18,7 @@ TODO - should we double-count papers? | Title | Description | Code | |-------|-------------|------| -| [Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates](https://arxiv.org/pdf/2005.00649.pdf)
Katherine A. Keith, David Jensen, and Brendan O’Connor | Survey paper about text as confounder studies. | | +| [Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates](https://arxiv.org/pdf/2005.00649.pdf)
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](http://ai.stanford.edu/blog/text-causal-inference/)
Reid Pryzant and Dan Jurafsky | Blog post about text as treatment (operationalized through lexicons) | | @@ -31,7 +31,9 @@ TODO - should we double-count papers? |-------|-------------|------| | [Challenges of Using Text Classifiers for Causal Inference](https://arxiv.org/pdf/1810.00956.pdf)
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](https://github.com/zachwooddoughty/emnlp2018-causal) | | [Deconfounded Lexicon Induction for Interpretable Social Science](https://nlp.stanford.edu/pubs/pryzant2018lexicon.pdf)
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](https://github.com/rpryzant/causal_attribution) | -| [How to Make Causal Inferences Using Texts](https://arxiv.org/pdf/1802.02163.pdf)
Naoki Egami, Christian J. Fong, Justin Grimmer, Margaret E. Roberts, and Brandon M. Stewart | Covers assumptions needed for text as treatment and concludes that you should use a train/test set. | | +| [How to Make Causal Inferences Using Texts](https://arxiv.org/pdf/1802.02163.pdf)
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](https://www.aclweb.org/anthology/P16-1151.pdf)
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](https://arxiv.org/pdf/1405.1438.pdf)
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 @@ -46,15 +48,31 @@ TODO - should we double-count papers? | Title | Description | Code | |-------|-------------|------| -| [Estimating Causal Effects of Tone in Online Debates](https://arxiv.org/pdf/1906.04177.pdf)
Dhanya Sridhar and Lise Getoor | Looks at effect of reply tone on the sentiment of subsiquent responses in online debates. | [git](https://github.com/dsridhar91/debate-causal-effects) | +| [Estimating Causal Effects of Tone in Online Debates](https://arxiv.org/pdf/1906.04177.pdf)
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](https://github.com/dsridhar91/debate-causal-effects) | | | | | -## Text as confounder +## Text as confounder (methods) | Title | Description | Code | |-------|-------------|------| -| [Adjusting for confounding with text matching](https://scholar.princeton.edu/sites/default/files/bstewart/files/textmatching_preprint.pdf)
Margaret Roberts, Brandon Stewart, and Richard Nielsen | Propose method for text matching via a combination of low-dimensional representations and propsensity scores. | | -| | | | +| [Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates](https://arxiv.org/pdf/2005.00649.pdf)
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](https://scholar.princeton.edu/sites/default/files/bstewart/files/textmatching_preprint.pdf)
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](https://arxiv.org/pdf/1801.00644)
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](http://www.jmlr.org/proceedings/papers/v48/johansson16.pdf)
Fredrik Johansson, Uri Shalit, David Sontag | One of their semi-synthetic experiments has news content as a confounder. | | + + +## Text as confounder (applications) +| Title | Description | Code | +|-------|-------------|------| +| [The language of social support in social media and its effect on suicidal ideation risk](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5565730/)
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](https://dl.acm.org/doi/pdf/10.1145/2858036.2858207?casa_token=ZJKLrg8LAOsAAAAA:ecs8HsunRyeUeD_De6Dx15_nPRZ1-mmjiXfAEXLpr25wwz6ywzQcJuZqWjJQIyibEGxZTOkULd1h)
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 | | +| [Estimating the effect of exercising on users online behavior](http://ls3.rnet.ryerson.ca/wiki/images/e/e0/Ossm2017-amin.pdf)
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](https://dl.acm.org/doi/pdf/10.1145/2998181.2998353?casa_token=U8iCSHz-uGUAAAAA:i9qcF0UCEH-lYKhTE9aA5RNMxFlvqfPW0tiHtUsh_lkmdV1F1O9ko9jPIl_nb8Cx5Rbtf4nn5JGq)
Alexandra Olteanu, Onur Varol, Emre Kiciman | Confouder: Past word use on Twitter. Confoudnig adjustment method: Stratified propensity score matching. || +| [A deep causal inference approach to measuring the effects of forming group loans in online non-profit microfinance platform](https://arxiv.org/pdf/1706.02795)
Thai T Pham and Yuanyuan Shen | Confounder: Microloan descriptions on Kiva. Confounding adjustment method: A-IPTW, TMLE on embeddings. || +| [Using longitudinal social media analysis to understand the effects of early college alcohol use](http://kiciman.org/wp-content/uploads/2018/10/college_alcohol_tweets_icwsm18e.pdf)
Emre Kiciman, Scott Counts, Melissa Gasser |Confounder: Previous posts on Twitter. Confounding adjustment method: Stratified propensity score matching. || +| [Estimating causal effects of exercise from mood logging data](https://linqs.soe.ucsc.edu/sites/default/files/papers/sridhar-causalml18_1.pdf)
Dhanya Sridhar, Aaron Springer, Victoria Hollis, Steve Whittaker, Lise Getoor |Confouder: Text of mood triggers. Confounding adjustment method: Propensity score matching || +| [A social media study on the effects of psychiatric medication use](https://www.aaai.org/ojs/index.php/ICWSM/article/download/3242/3110/)
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.| +| [Influence via Ethos: On the Persuasive Power of Reputation in Deliberation Online](https://arxiv.org/pdf/2006.00707.pdf)
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.|| # Causality to Improve NLP @@ -63,9 +81,8 @@ TODO - should we double-count papers? | Title | Description | Code | |-------|-------------|------| -| [CausaLM: Causal Model Explanation Through Counterfactual Language Models](https://arxiv.org/pdf/2005.13407.pdf)
Amir Feder, Nadav Oved, Uri Shalit and Roi Reichart | Suggested a method for generating causal explanations through counterfactual language representations. | [git](https://github.com/amirfeder/CausaLM) | -| [Causal Mediation Analysis for Interpreting Neural NLP: The Case of Gender Bias](https://arxiv.org/pdf/2004.12265.pdf)
Jesse Vig, Sebastian Gehrmann, Yonatan Belinkov, Sharon Qian, Daniel Nevo, Yaron Singer and Stuart Shieber | Uses causal mediation analysis to interpret NLP models. | [git](https://github.com/sebastianGehrmann/CausalMediationAnalysis) | - +| | | | +| | | | ## Sensitivity and Robustness @@ -75,7 +92,7 @@ TODO - should we double-count papers? | | | | -# Social Science Applications +# Applications in the Social Sciences ## Marketing @@ -91,18 +108,21 @@ TODO - should we double-count papers? | Title | Description | Code | |-------|-------------|------| -| [Let’s Make Your Request More Persuasive: Modeling Persuasive Strategies via Semi-Supervised Neural Nets on Crowdfunding Platforms](https://www.aclweb.org/anthology/N19-1364.pdf)
Diyi Yang, Jiaao Chen, Zichao Yang, Dan Jurafsky, and Eduard Hovy | Found persuasive strategies that cause higher crowdfunding donation rates. | [git](https://github.com/jiaaoc/Persuasion_Strategy) | +| | | | | | | | -## Mental Health / Social Good +## Framing | Title | Description | Code | |-------|-------------|------| -| [Quantifying the Causal Efects of Conversational Tendencies](https://www.cs.cornell.edu/~cristian/Causal_effects_of_conversational_tendencies_files/causality_conversations.pdf)
Zhang, Justine, Sendhil Mullainathan, and Cristian Danescu-Niculescu-Mizil | Studied the effects of "conversational tendencies" (meta attributes like message length, response time, etc) on crisis counseling outcomes (whether the conversation was helpful or not to the client). | | | | | | +| | | | + +## Mental Health ## Psychology +https://doi.org/10.1037/hea0001025 | Title | Description | Code | |-------|-------------|------|