Multivariate Matching Methods That Are Monotonic Imbalance Bounding

By Stefano Maria Iacus, Gary King, Giuseppe Porro. 2011

969 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistics and Probability

Citations in the corpus

FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference (2017, background): “It bypasses the need to ﬁt complicated propensity score models by coarsening or discretizing covariates in such a way that the newly constructed covariates allow for exact matching (Iacus et al., 2011a,b).”
How Anticipated Employee Mobility Affects Acquisition Likelihood: Evidence from a Natural Experiment (2015, methodology): “CEM is amultivariate matching technique that is monotonic imbalance bounding (Iacus, King, and Porro, 2011) and, as such, reduces causal estimation error, model-dependence, bias, and inefficiency (Iacus, King, and Porro, 2009a, b).”
The Real Effects of Real Earnings Management: Evidence from Innovation (2018, methodology): “We use coarsened exact matching as introduced in Blackwell et al. (2009) and Iacus et al. (2011).”
The Effect of Cultural Similarity on Mergers and Acquisitions: Evidence from Corporate Social Responsibility (2018, positive): “To achieve this, we employ the Coarsened Exact Matching (CEM) methodology (Blackwell, Iacus, King, and Porro, 2011) which allows ex-ante balance between the treated and control groups.”
Independent Boards and Innovation (2017, background): “9 CEM has several features that bound the degree of model dependence, reduce causal estimation error, bias, and inefficiency (Iacus, King, and Porro, 2009a, 2009b, 2011, for a similar application, see Azoulay, Zivin, and Wang 2010).”
Adjusting for Confounding with Text Matching (2020, methodology): “In this way, matching on topics can be seen as a high-dimensional analog to the coarsening step in coarsened exact matching (Iacus, King, and Porro 2011).”
How Effective Were the Federal Reserve Emergency Liquidity Facilities? Evidence from the Asset‐Backed Commercial Paper Money Market Mutual Fund Liquidity Facility (2013, methodology): “Using the Coarsened Exact Matching (CEM) method (Iacus, King, and Porro (2011a, 2011b)), we match funds that hold ABCP in their portfolios with funds that hold no ABCP but are comparable in terms of their portfolio risk.”
Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation (2020, methodology): “Coarsened exact matching (Iacus et al. 2011; Iacus et al. 2012), for example, can require a user-defined coarsening of a high dimensional covariate space, which can be error-prone.”
Hypothesis Tests That Are Robust to Choice of Matching Method (2018, background): “In practice, several types of constraints on the quality of matches, chiefly balance, as defined in Equation (5), can be implemented as a coarsening choice on the covariates (Iacus et al. 2011), and as such as exclusively binning constraints.”
Propensity Score Analysis: Recent Debate and Discussion (2020, methodology): “The issues King and Nielsen raised are relevant to researchers from all professions and disciplines: What are the statistical problems pertaining to propensity score matching (PSM), what are the best alternatives given the range of available propensity score methods and the rangeof data generation s”
Creativity at the Knowledge Frontier: The Impact of Specialization in Fast- and Slow-paced Domains* (2018, methodology): “To construct the matched sample, we used a one-to-one coarsened exact matching (CEM) method (Blackwell et al., 2009; Iacus et al., 2011) based on mathematicians’ publication records in the pre-collapse period.5 The matched sample contains data on 4,076 mathematicians, of whom 2,038 are specialists…”
Can You Gig It? An Empirical Examination of the Gig Economy and Entrepreneurial Activity (2018, background): “Third, ignoring heterogeneity between treated and control groups exacerbates the potential that our results are dependent on our choice of model specification (Iacus et al. 2011).”
Causal Inference without Balance Checking: Coarsened Exact Matching (2012, methodology): “Examples of other matching methods that meet the congruence principle include Iacus and Porro (2007, 2008).”
Interpretable Almost-Exact Matching for Causal Inference (2019, methodology): “For example, Coarsened Exact Matching (Iacus et al., 2012, 2011) is almost-exact if one were to use an oracle (should one ever become available) that bins covariates according to importance for estimating causal effects.”
Government Green Procurement Spillovers: Evidence from Municipal Building Policies in California (2014, background): “CEM weights are described in Iacus et al. (2012) and discussed in the text. t-Statistics corresponding to po10% are in boldface. T. Simcoe, M.W. Toffel / Journal of Environmental Economics and Management 68 (2014) 411–434 421”
Why Propensity Scores Should Not Be Used for Matching (2019, background): “…for the PSM Paradox is because, after PSM achieves its goals of finding a subset of the data that approximates complete randomization, with approximately constant propensity scores within strata, any further pruning is being done essentially at random, exactly as a completely randomized experiment.”