Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference

@article{Ho2007MatchingAN,
  title={Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference},
  author={Daniel E. Ho and Kosuke Imai and Gary King and Elizabeth A. Stuart},
  journal={Political Analysis},
  year={2007},
  volume={15},
  pages={199 - 236}
}
Although published works rarely include causal estimates from more than a few model specifications, authors usually choose the presented estimates from numerous trial runs readers never see. Given the often large variation in estimates across choices of control variables, functional forms, and other modeling assumptions, how can researchers ensure that the few estimates presented are accurate or representative? How do readers know that publications are not merely demonstrations that it is… 
A Theory of Statistical Inference for Matching Methods in Applied Causal Research ∗
Matching methods for causal inference have become a popular way of reducing model dependence and bias, in large part because of their convenience and conceptual simplicity. Researchers most commonly
A Theory of Statistical Inference for Matching Methods in Causal Research
TLDR
The theory of inference is presented, which makes it possible for researchers to treat matching as a simple form of preprocessing to reduce model dependence, after which all the familiar inferential techniques and uncertainty calculations can be applied.
How Coarsening Simplifies Matching-Based Causal Inference Theory ∗
TLDR
It is shown how conceptualizing continuous variables as having logical breakpoints (such as phase transitions when measuring temperature or high school or college degrees in years of education) is both natural substantively and can be used to simplify causal inference theory.
Robust Testing for Causal Inference in Observational Studies
A vast number of causal inference studies use matching techniques, where treatment cases are matched with similar control cases. For observational data in particular, we claim there is a major source
The causal interpretation of estimated associations in regression models
Abstract A common causal identification strategy in political science is selection on observables. This strategy assumes one observes a set of covariates that is, after statistical adjustment,
Choosing an Identifying Set of Matching or Conditioning Variables ∗
Political scientists estimate average causal effects with regression or matching techniques, but both techniques require the user to choose a set of matching or conditioning variables. In this paper,
Hypothesis Tests That Are Robust to Choice of Matching Method
TLDR
Methodology based on discrete optimization to create robust tests that explicitly account for variation in the quality of matches in causal inference studies is provided.
Telescope Matching : Reducing Model Dependence in the Estimation of Direct Effects *
Matching methods are widely used to reduce the dependence of causal inferences on modeling assumptions, but their application has been mostly limited to the overall effect of a single treatment.
When Can History Be Our Guide? The Pitfalls of Counterfactual Inference
TLDR
This paper develops easy-to-apply methods to evaluate counterfactuals that do not require sensitivity testing over specified classes of models and finds evidence that many scholars are inadvertently drawing conclusions based more on modeling hypotheses than on evidence in the data.
Robust Post-Matching Inference
TLDR
It is shown that ignoring the matching step results in asymptotically valid standard errors if matching is done without replacement and the regression model is correctly specified relative to the population regression function of the outcome variable on the treatment variable and all the covariates used for matching.
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 273 REFERENCES
When Can History Be Our Guide? The Pitfalls of Counterfactual Inference
TLDR
This paper develops easy-to-apply methods to evaluate counterfactuals that do not require sensitivity testing over specified classes of models and finds evidence that many scholars are inadvertently drawing conclusions based more on modeling hypotheses than on evidence in the data.
The Dangers of Extreme Counterfactuals
TLDR
A proof that inferences farther from the data allow more model dependence is offered and easy-to-apply methods to evaluate how model dependent the authors' answers would be to specified counterfactuals are developed.
6. Matching with Multiple Controls to Estimate Treatment Effects in Observational Studies
Matching to control for covariates in the estimation of treatment effects is not common in sociology, where multivariate data are most often analyzed using multiple regression and its
Discussion: Efficiency and Self‐efficiency With Multiple Imputation Inference
Summary By closely examining the examples provided in Nielsen (2003), this paper further explores the relationship between self-efficiency (Meng, 1994) and the validity of Rubin's multiple
Making the Most Of Statistical Analyses: Improving Interpretation and Presentation
TLDR
This article offers an approach, built on the technique of statistical simulation, to extract the currently overlooked information from any statistical method and to interpret and present it in a reader-friendly manner.
Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation
We propose a remedy for the discrepancy between the way political scientists analyze data with missing values and the recommendations of the statistics community. Methodologists and statisticians
Characterizing the effect of matching using linear propensity score methods with normal distributions
SUMMARY Matched sampling is a standard technique for controlling bias in observational studies due to specific covariates. Since Rosenbaum & Rubin (1983), multivariate matching methods based on
Large Sample Properties of Matching Estimators for Average Treatment Effects
Matching estimators for average treatment effects are widely used in evaluation research despite the fact that their large sample properties have not been established in many cases. The absence of
Random Recursive Partitioning: A Matching Method for the Estimation of the Average Treatment Effect
In this paper we introduce the Random Recursive Partitioning (RRP) matching method. RRP generates a proximity matrix which might be useful in econometric applications like average treatment effect
Misunderstandings between experimentalists and observationalists about causal inference
Summary. We attempt to clarify, and suggest how to avoid, several serious misunderstandings about and fallacies of causal inference. These issues concern some of the most fundamental advantages and
...
1
2
3
4
5
...