| Title |
Quantifying causality in data science with quasi-experiments
|
|---|---|
| Published in |
Nature Computational Science, January 2021
|
| DOI | 10.1038/s43588-020-00005-8 |
| Pubmed ID | |
| Authors |
Tony Liu, Lyle Ungar, Konrad Kording |
X Demographics
The data shown below were collected from the profiles of 83 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 12 | 14% |
| United States | 7 | 8% |
| Germany | 6 | 7% |
| Italy | 3 | 4% |
| Mexico | 3 | 4% |
| Australia | 2 | 2% |
| Greece | 1 | 1% |
| Belgium | 1 | 1% |
| France | 1 | 1% |
| Other | 6 | 7% |
| Unknown | 41 | 49% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 61 | 73% |
| Scientists | 19 | 23% |
| Practitioners (doctors, other healthcare professionals) | 2 | 2% |
| Science communicators (journalists, bloggers, editors) | 1 | 1% |
Mendeley readers
The data shown below were compiled from readership statistics for 162 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 162 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 33 | 20% |
| Student > Ph. D. Student | 22 | 14% |
| Student > Master | 20 | 12% |
| Student > Bachelor | 9 | 6% |
| Professor | 8 | 5% |
| Other | 26 | 16% |
| Unknown | 44 | 27% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 19 | 12% |
| Psychology | 10 | 6% |
| Economics, Econometrics and Finance | 9 | 6% |
| Engineering | 9 | 6% |
| Medicine and Dentistry | 8 | 5% |
| Other | 51 | 31% |
| Unknown | 56 | 35% |
Attention Score in Context
This research output has an Altmetric Attention Score of 46. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 November 2021.
All research outputs
#928,661
of 25,793,330 outputs
Outputs from Nature Computational Science
#79
of 609 outputs
Outputs of similar age
#26,279
of 534,595 outputs
Outputs of similar age from Nature Computational Science
#9
of 35 outputs
Altmetric has tracked 25,793,330 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 609 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 25.6. This one has done well, scoring higher than 87% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 534,595 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 95% of its contemporaries.
We're also able to compare this research output to 35 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 74% of its contemporaries.