Nonparametric Identification of Causal Effects Under Temporal Dependence

2014 ◽  
Author(s):  
Allan Dafoe
Keyword(s):  
Causal Effects ◽  
Nonparametric Identification ◽  
Temporal Dependence

scholarly journals Endogeneity and Non-Response Bias in Treatment Evaluation: Nonparametric Identification of Causal Effects by Instruments

2015 ◽  
Author(s):  
Hans Fricke ◽  
Markus Frölich ◽  
Martin Huber ◽  
Michael Lechner
Keyword(s):  
Response Bias ◽  
Treatment Evaluation ◽  
Causal Effects ◽  
Nonparametric Identification

scholarly journals Proxy variables and nonparametric identification of causal effects

2017 ◽  
Vol 150 ◽  
pp. 152-154
Author(s):  
Xavier de Luna ◽  
Philip Fowler ◽  
Per Johansson
Keyword(s):  
Causal Effects ◽  
Nonparametric Identification ◽  
Proxy Variables

scholarly journals Proxy Variables and Nonparametric Identification of Causal Effects

2016 ◽  
Author(s):  
Xavier de Luna ◽  
Philip Fowler ◽  
Per Johansson
Keyword(s):  
Causal Effects ◽  
Nonparametric Identification ◽  
Proxy Variables

scholarly journals Endogeneity and non‐response bias in treatment evaluation – nonparametric identification of causal effects by instruments

2020 ◽  
Vol 35 (5) ◽  
pp. 481-504
Author(s):  
Hans Fricke ◽  
Markus Frölich ◽  
Martin Huber ◽  
Michael Lechner
Keyword(s):  
Response Bias ◽  
Treatment Evaluation ◽  
Causal Effects ◽  
Nonparametric Identification

scholarly journals Testing for the Unconfoundedness Assumption Using an Instrumental Assumption

2014 ◽  
Vol 2 (2) ◽  
pp. 187-199 ◽  
Author(s):  
Xavier de Luna ◽  
Per Johansson
Keyword(s):  
Simulation Study ◽  
Instrumental Variable ◽  
Observational Studies ◽  
Causal Effect ◽  
Causal Effects ◽  
Nonparametric Identification ◽  
Average Causal Effect

AbstractThe identification of average causal effects of a treatment in observational studies is typically based either on the unconfoundedness assumption (exogeneity of the treatment) or on the availability of an instrument. When available, instruments may also be used to test for the unconfoundedness assumption. In this paper, we present a set of assumptions on an instrumental variable which allows us to test for the unconfoundedness assumption, although they do not necessarily yield nonparametric identification of an average causal effect. We propose a test for the unconfoundedness assumption based on the instrumental assumptions introduced and give conditions under which the test has power. We perform a simulation study and apply the results to a case study where the interest lies in evaluating the effect of job practice on employment.

Nonparametric Identification of Causal Effects under Temporal Dependence

2015 ◽  
Vol 47 (2) ◽  
pp. 136-168 ◽  
Author(s):  
Allan Dafoe
Keyword(s):  
Causal Effect ◽  
Causal Effects ◽  
Correct Identification ◽  
Social Scientists ◽  
Temporal Dependence ◽  
Incorrect Decision ◽  
Causal Assumptions ◽  
Common Causes ◽  
Dependent Variables ◽  
Identification Strategy

Social scientists routinely address temporal dependence by adopting a simple technical fix. However, the correct identification strategy for a causal effect depends on causal assumptions. These need to be explicated and justified; almost no studies do so. This article addresses this shortcoming by offering a precise general statement of the (nonparametric) causal assumptions required to identify causal effects under temporal dependence. In particular, this article clarifies when one should condition or not condition on lagged dependent variables (LDVs) to identify causal effects: one should not condition on LDVs, if there is no reverse causation and no outcome autocausation; one should condition on LDVs if there are no unobserved common causes of treatment and the lagged outcome, or no unobserved persistent causes of the outcome. When only one of these is true (with one exception), the incorrect decision will induce bias. Absent a well-justified identification strategy, inferences should be appropriately qualified.

scholarly journals Causal inference with confounders missing not at random

Biometrika ◽  
2019 ◽  
Vol 106 (4) ◽  
pp. 875-888
Author(s):  
S Yang ◽  
L Wang ◽  
P Ding
Keyword(s):  
Missing Data ◽  
Causal Inference ◽  
Least Squares ◽  
Observational Studies ◽  
Missing Values ◽  
Least Squares Estimator ◽  
Causal Effects ◽  
Nonparametric Identification ◽  
Two Stage ◽  
Missing Not At Random

Summary It is important to draw causal inference from observational studies, but this becomes challenging if the confounders have missing values. Generally, causal effects are not identifiable if the confounders are missing not at random. In this article we propose a novel framework for nonparametric identification of causal effects with confounders subject to an outcome-independent missingness, which means that the missing data mechanism is independent of the outcome, given the treatment and possibly missing confounders. We then propose a nonparametric two-stage least squares estimator and a parametric estimator for causal effects.

Sign in / Sign up

Export Citation Format

Share Document