Merge pull request #2 from causaltext/kk

Adding confounders papers
causaltext · Oct 2, 2020 · bccb711 · bccb711
2 parents f18edcf + 7af10d1
commit bccb711
Showing 1 changed file with 33 additions and 13 deletions.
diff --git 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) <br> 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) <br> 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/) <br> 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) <br> 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) <br>  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) <br> 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) <br> 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) <br> 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) <br> 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) <br> 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) <br> 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) <br> 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) <br> 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) <br> 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) <br> 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) <br> 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/) <br> 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) <br> 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) <br> 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) <br> 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) <br> 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) <br> 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) <br> 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/) <br> 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) <br> 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) <br> 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) <br> 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)    |
-
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+|       |             |      |
 
 ## Sensitivity and Robustness
 
@@ -75,7 +92,7 @@ TODO - should we double-count papers?
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-# 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) <br> 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) <br> 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 |
 |-------|-------------|------|