The Landscape of Causal Inference: Perspective From Citation Network Analysis

<p>Causal inference is a fast-growing multidisciplinary field that has drawn extensive interests from statistical sciences and health and social sciences. In this article, we gather comprehensive information on publications and citations in causal inference and provide a review of the field from the perspective of citation network analysis. We provide descriptive analyses by showing the most cited publications, the most prolific and the most cited authors, and structural properties of the citation network. Then, we examine the citation network through exponential random graph models (ERGMs). We show that both technical aspects of the publications (e.g., publication length, time and quality) and social processes such as homophily (the tendency to cite publications in the same field or with shared authors), cumulative advantage, and transitivity (the tendency to cite references’ references), matter for citations. We also provide specific analysis of citations among the top authors in the field and present a ranking and clustering of the authors. Overall, our article reveals new insights into the landscape of the field of causal inference and may serve as a case study for analyzing citation networks in a multidisciplinary field and for fitting ERGMs on big networks. Supplementary materials for this article are available online.</p

Author Credit for Transdisciplinary Collaboration

<div><p>Transdisciplinary collaboration is the key for innovation. An evaluation mechanism is necessary to ensure that academic credit for this costly process can be allocated fairly among coauthors. This paper proposes a set of quantitative measures (e.g., t_credit and t_index) to reflect authors’ transdisciplinary contributions to publications. These measures are based on paper-topic probability distributions and author-topic probability distributions. We conduct an empirical analysis of the information retrieval domain which demonstrates that these measures effectively improve the results of harmonic_credit and h_index measures by taking into account the transdisciplinary contributions of authors. The definitions of t_credit and t_index provide a fair and effective way for research organizations to assign credit to authors of transdisciplinary publications.</p></div

Segmented cumulative counts of the 100 top-ranked authors based on harmonic_credit.

<p>Note: The segments are not separated evenly because tied ranking cases are not uncommon in ranking lists. In each cell, the number means the counts of authors who have the same rank range in both ranking list of harmonic_credit and ranking list of t_credit_sum, and the percentage in brackets means the percentage of the counts of authors who have the same range in both ranking list.</p><p>Segmented cumulative counts of the 100 top-ranked authors based on harmonic_credit.</p

Segmented counts of the 100 top-ranked authors based on harmonic_credit.

<p>Note: The segments are not separated evenly because tied ranking cases are not uncommon in ranking lists. In each cell, the number means the counts of authors who have the same rank range in both ranking list of harmonic_credit and ranking list of t_credit_sum, and the percentage in brackets means the percentage of the counts of authors who have the same range in both ranking list.</p><p>Segmented counts of the 100 top-ranked authors based on harmonic_credit.</p

Segmented cumulative counts of the 106 top-ranked authors based on h_index.

<p>Note: The segments are not separated evenly because tied ranking cases are not uncommon in ranking lists. In each cell, the number means the counts of authors who have the same rank range in both ranking list of h_index and ranking list of t_index, and the percentage in brackets means the percentage of the counts of authors who have the same range in both ranking list.</p><p>Segmented cumulative counts of the 106 top-ranked authors based on h_index.</p

Difference in ranks of the 20 top-ranked authors based on the harmonic_credit schema and t_credit schema.

<p>Note: No. of pub.Number of publications; F pub.(%) Papers as first author; Rank change Rank-order change of harmonic_credit_sum vs. t_credit_sum; in Rank change column, zero indicates that rank order does not change, a positive number indicates that rank order increases, and a negative number indicates a decrease.</p><p>Difference in ranks of the 20 top-ranked authors based on the harmonic_credit schema and t_credit schema.</p

The comparision between ranks of t_index and h_index.

<p>The comparision between ranks of t_index and h_index.</p

Segmented counts of the 106 top-ranked authors based on h_index.

<p>Note: The segments are not separated evenly because tied ranking cases are not uncommon in ranking lists. In each cell, the number means the counts of authors who have the same rank range in both ranking list of h_index and ranking list of t_index, and the percentage in brackets means the percentage of the counts of authors who have the same range in both ranking list.</p><p>Segmented counts of the 106 top-ranked authors based on h_index.</p

The representative credit-assignment schemas’ distribution.

<p>The representative credit-assignment schemas’ distribution.</p

Examples of evaluating the author transdisciplinary contribution.

<p>Examples of evaluating the author transdisciplinary contribution.</p